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878, 885, 901, 908, 911, 913, 914, 915, 916, 989, 990, 992, 993, 995, 997, 999, 1000, 1001, 1006, 1010, 1013, 1020, 1021, 1022, 1024, 1026, 1032, 1034, 1040, 1041, 1044, 1052], "complexity_comput": [47, 50], "complexity_label": [47, 50], "compli": [371, 385, 942, 1020], "complianc": [149, 371], "compliant": [317, 371, 1049, 1050, 1051, 1052], "complic": [65, 239, 298, 377, 413, 694, 1003], "compon": [2, 11, 44, 45, 56, 79, 91, 95, 102, 104, 105, 114, 115, 119, 121, 122, 124, 125, 127, 128, 130, 152, 172, 180, 182, 194, 214, 225, 236, 237, 240, 244, 248, 249, 250, 252, 253, 254, 256, 262, 264, 269, 283, 284, 285, 286, 294, 295, 301, 308, 314, 332, 351, 358, 363, 366, 371, 373, 377, 380, 397, 399, 403, 404, 406, 411, 413, 415, 417, 433, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 458, 459, 461, 465, 466, 467, 468, 469, 470, 471, 472, 478, 479, 480, 481, 499, 501, 512, 518, 523, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 570, 579, 580, 581, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 600, 601, 609, 610, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 626, 627, 628, 629, 630, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 687, 688, 689, 690, 691, 692, 695, 798, 799, 800, 801, 804, 805, 815, 823, 831, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 861, 862, 863, 865, 866, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 895, 896, 897, 898, 899, 900, 901, 902, 905, 906, 907, 908, 909, 910, 911, 913, 914, 915, 916, 920, 948, 949, 992, 997, 999, 1010, 1012, 1015, 1019, 1020, 1021, 1022, 1024, 1025, 1026, 1031, 1033, 1037, 1038, 1039, 1040, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050], "component_1": 253, "component_2": 253, "component_indices_": [638, 1044], "components_": [46, 55, 91, 104, 116, 120, 122, 123, 130, 236, 237, 244, 262, 301, 308, 316, 385, 404, 408, 440, 528, 529, 530, 531, 533, 534, 535, 536, 537, 538, 540, 541, 638, 854, 861, 897, 898, 992, 1012, 1035, 1037, 1043, 1046, 1049], "components_col": 105, "compos": [2, 44, 45, 63, 101, 102, 103, 107, 116, 136, 143, 154, 180, 183, 184, 185, 206, 208, 223, 234, 242, 244, 246, 277, 280, 309, 313, 315, 316, 317, 319, 320, 384, 394, 403, 404, 407, 460, 461, 462, 463, 512, 550, 611, 999, 1001, 1021, 1031], "composit": [7, 36, 234, 313, 363, 407, 657, 789, 990, 996, 1009, 1026, 1032], "compound": [44, 223, 411, 609, 611, 679, 725, 753, 996], "compoundkernel": [2, 609, 1045], "comprehens": [339, 379, 411, 759, 760, 998, 1024, 1039], "compress": [43, 51, 56, 99, 103, 139, 143, 154, 162, 180, 183, 184, 234, 244, 280, 303, 313, 316, 366, 397, 403, 408, 411, 412, 651, 671, 692, 835, 878, 970, 973, 985, 996, 1001, 1010, 1021, 1031, 1040], "compressed_raccoon_kmean": 86, "compressed_raccoon_uniform": 86, "compris": [102, 146, 261, 346, 347, 348, 366, 383, 408, 410, 512, 808, 997], "compromis": [49, 65, 184, 358, 371, 646, 678, 1003, 1034], "comput": [2, 28, 44, 46, 47, 51, 52, 53, 54, 59, 64, 73, 76, 77, 80, 85, 87, 90, 91, 93, 94, 102, 103, 104, 110, 111, 112, 113, 121, 129, 137, 139, 141, 143, 144, 145, 146, 147, 148, 149, 151, 154, 155, 156, 160, 162, 163, 167, 172, 174, 175, 178, 183, 184, 185, 186, 188, 191, 192, 194, 195, 196, 197, 198, 206, 208, 210, 213, 219, 222, 223, 226, 228, 229, 233, 234, 235, 236, 238, 242, 243, 244, 245, 246, 253, 257, 259, 261, 262, 263, 264, 265, 266, 270, 272, 273, 274, 275, 277, 283, 285, 287, 289, 290, 292, 293, 296, 301, 303, 309, 312, 313, 314, 316, 317, 319, 320, 322, 325, 327, 328, 335, 339, 342, 346, 347, 348, 353, 359, 360, 365, 366, 368, 371, 373, 376, 377, 378, 380, 383, 384, 385, 388, 389, 397, 398, 399, 400, 401, 403, 405, 406, 408, 409, 410, 411, 412, 413, 414, 415, 416, 433, 434, 435, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 448, 453, 455, 457, 458, 459, 461, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 479, 513, 528, 529, 531, 532, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 564, 565, 566, 567, 571, 580, 583, 584, 587, 588, 589, 591, 592, 597, 598, 601, 602, 603, 604, 605, 606, 607, 609, 611, 612, 613, 614, 615, 618, 619, 620, 621, 622, 623, 624, 626, 628, 629, 630, 631, 632, 633, 636, 637, 638, 639, 640, 641, 643, 644, 645, 646, 647, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 670, 671, 672, 673, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 712, 713, 715, 716, 717, 718, 719, 720, 726, 727, 728, 730, 731, 732, 735, 737, 739, 740, 741, 744, 755, 756, 757, 758, 759, 760, 761, 762, 764, 765, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 787, 788, 789, 790, 793, 794, 795, 797, 798, 799, 800, 801, 804, 805, 807, 815, 823, 824, 826, 827, 828, 829, 830, 832, 833, 834, 835, 845, 846, 847, 848, 849, 850, 851, 852, 853, 855, 856, 857, 858, 859, 861, 862, 863, 870, 871, 874, 875, 880, 881, 882, 883, 884, 885, 889, 890, 892, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 905, 907, 910, 912, 913, 914, 915, 916, 946, 947, 948, 949, 966, 967, 972, 974, 980, 989, 992, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1008, 1010, 1012, 1013, 1014, 1015, 1016, 1019, 1020, 1024, 1026, 1028, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052], "computation": [38, 54, 109, 120, 140, 145, 154, 167, 283, 339, 346, 357, 372, 383, 403, 404, 407, 408, 410, 413, 535, 631, 632, 670, 764, 801, 803, 804, 805, 815, 828, 996, 997, 999, 1007, 1008, 1012, 1026, 1035], "computationallyexpens": [103, 104, 244, 253, 257, 261, 262, 263, 275], "compute_class_weight": [2, 385, 1034, 1045, 1051], "compute_corrected_ttest": 263, "compute_dist": [437, 441, 1043], "compute_full_tre": [437, 441, 1035], "compute_import": 1033, "compute_inverse_compon": [897, 898, 1012], "compute_inverse_transform": 1045, "compute_label": [438, 445], "compute_node_depth": 353, "compute_optics_graph": [2, 451, 452, 1048], "compute_sample_weight": [2, 1045], "compute_scor": [107, 127, 190, 191, 643, 644, 1040], "compute_score_for": 178, "compute_sourc": 415, "computed_scor": 644, "con": [397, 587, 999], "concat": [44, 103, 139, 143, 154, 178, 182, 183, 184, 198, 223, 234, 244, 313, 316, 878], "concaten": [2, 64, 71, 83, 94, 100, 101, 103, 104, 112, 136, 139, 143, 150, 154, 159, 175, 180, 183, 184, 185, 190, 193, 200, 219, 220, 226, 232, 234, 244, 248, 252, 253, 259, 268, 270, 271, 272, 273, 288, 301, 307, 309, 310, 313, 316, 325, 334, 338, 346, 404, 460, 463, 501, 506, 528, 534, 538, 539, 597, 782, 801, 864, 865, 867, 870, 878, 910, 1001, 1021, 1032, 1051], "concav": [163, 320, 368], "concentr": [47, 49, 98, 118, 125, 134, 152, 172, 179, 180, 230, 247, 249, 254, 293, 305, 326, 367, 371, 410, 439, 516, 798, 999, 1006, 1021], "concentrations_prior": 248, "concept": [2, 112, 140, 144, 239, 272, 383, 403, 409, 411, 992, 1000, 1003, 1016, 1018, 1024], "conceptu": [368, 410, 998], "concern": [38, 57, 72, 108, 114, 117, 119, 131, 133, 157, 164, 166, 177, 180, 187, 189, 224, 247, 253, 257, 279, 281, 284, 297, 302, 323, 330, 345, 349, 358, 373, 397, 997, 1012], "concis": [65, 206, 371, 376, 1042, 1044], "conclud": [67, 134, 183, 191, 223, 263, 316, 348, 354, 386, 865], "conclus": [44, 125, 183, 185, 206, 208, 263, 265, 354, 410], "concomit": [648, 996], "concret": [210, 372, 386, 402, 412, 673, 674, 897, 898, 996, 1014, 1019, 1050], "concurr": [385, 411, 967, 1043, 1045], "cond": 1050, "conda": [312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 359, 371, 372, 374, 375, 377, 379, 389, 390, 396, 397, 1016], "conda_prefix": 377, "condarc": 369, "condens": [188, 442], "condit": [2, 44, 52, 53, 63, 65, 104, 113, 139, 143, 146, 154, 180, 181, 183, 198, 200, 208, 211, 223, 239, 243, 244, 262, 266, 309, 315, 353, 377, 383, 388, 399, 400, 401, 402, 403, 405, 408, 410, 412, 447, 460, 467, 468, 469, 474, 493, 510, 520, 521, 533, 536, 537, 538, 540, 542, 544, 546, 547, 559, 626, 631, 632, 642, 649, 650, 651, 653, 654, 655, 669, 671, 672, 673, 674, 681, 682, 686, 697, 712, 717, 718, 737, 738, 796, 822, 840, 841, 842, 843, 844, 863, 866, 869, 878, 879, 882, 886, 949, 971, 994, 996, 997, 998, 1000, 1002, 1003, 1005, 1010, 1016, 1021, 1026, 1032, 1034, 1035, 1036, 1038, 1039, 1041, 1043, 1045, 1046, 1047, 1049, 1052], "condition": [52, 65, 206, 401, 405, 759, 994, 1000], "condition2": 154, "conditionedsystem": 52, "conduct": [182, 263, 413, 1023, 1045], "conf": [47, 65, 375, 401, 407, 840, 1002, 1045], "confer": [257, 263, 366, 403, 408, 414, 433, 435, 440, 446, 508, 532, 560, 696, 708, 727, 757, 861, 862, 863, 1000, 1006, 1012, 1016], "confid": [53, 62, 63, 64, 65, 67, 149, 172, 174, 244, 245, 249, 261, 263, 266, 314, 327, 386, 401, 413, 635, 657, 658, 665, 667, 670, 673, 674, 675, 698, 702, 707, 720, 727, 728, 740, 741, 757, 790, 833, 872, 905, 907, 910, 996, 999, 1000, 1001, 1006, 1013, 1014, 1015, 1024, 1041], "config": [53, 359, 369, 371, 372, 379, 625, 1039], "config_context": [2, 246, 321, 358, 359, 399, 625, 903, 1038, 1044, 1047], "configur": [2, 3, 47, 50, 65, 67, 103, 104, 139, 143, 154, 183, 184, 234, 239, 244, 246, 257, 277, 310, 313, 316, 346, 357, 369, 371, 373, 377, 383, 385, 389, 394, 399, 404, 411, 412, 427, 433, 438, 439, 440, 441, 443, 445, 448, 458, 460, 461, 464, 478, 479, 480, 481, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 579, 580, 581, 587, 588, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 609, 610, 625, 626, 627, 628, 629, 631, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 688, 689, 690, 691, 692, 694, 695, 789, 800, 801, 802, 803, 808, 810, 815, 819, 823, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 847, 848, 849, 850, 852, 854, 855, 856, 857, 861, 862, 863, 864, 865, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 897, 898, 900, 901, 903, 905, 906, 907, 908, 909, 910, 911, 913, 914, 915, 916, 967, 968, 997, 1000, 1010, 1015, 1019, 1026, 1035, 1038, 1039, 1040, 1041, 1043, 1045, 1046, 1047, 1048, 1052], "confirm": [44, 53, 67, 116, 137, 143, 146, 149, 162, 185, 206, 208, 257, 266, 269, 272, 308, 348, 375, 989, 1010, 1039], "conflict": [369, 374, 375, 379, 389, 1038, 1039], "conform": [53, 371, 579, 627, 833, 834, 1000, 1019, 1020, 1038, 1044], "confound": [182, 183], "confus": [2, 69, 180, 233, 255, 257, 272, 324, 325, 346, 385, 464, 501, 630, 651, 697, 713, 715, 718, 730, 731, 739, 755, 785, 788, 831, 903, 910, 1021, 1031, 1032, 1036, 1040, 1041, 1043, 1044, 1045, 1046], "confusingli": 369, "confusion_matrix": [2, 69, 233, 256, 257, 320, 324, 325, 399, 697, 713, 755, 800, 1000, 1032, 1037, 1038, 1041, 1042, 1044, 1048, 1052], "confusion_matrix_scor": 1000, "confusionmatrixdisplai": [2, 46, 69, 256, 315, 320, 324, 346, 630, 718, 1000, 1041, 1042, 1044, 1045, 1046, 1050], "congruenc": [653, 654, 655, 681, 682], "conjug": [183, 263, 448, 458, 671, 673, 686, 695, 996], "conjunct": [394, 403, 592, 702, 807, 823, 824, 826, 827, 828, 829, 832, 968, 990, 996, 1046], "connect": [2, 52, 79, 81, 84, 87, 95, 99, 369, 371, 380, 385, 405, 437, 441, 448, 458, 459, 583, 584, 695, 847, 848, 849, 851, 853, 855, 856, 857, 858, 859, 998, 1000, 1003, 1005, 1013, 1023, 1035, 1044, 1048], "connected_compon": 1038, "connectionist": [862, 863], "conner": 1044, "connor": [1039, 1044, 1048, 1049, 1051], "connossor": [1039, 1040], "conocophillip": 52, "conort": 1024, "conquer": 949, "conrad": [1031, 1032, 1046, 1049, 1050], "conroi": 1046, "consecut": [134, 144, 207, 243, 383, 401, 407, 411, 439, 443, 445, 446, 448, 452, 455, 458, 534, 535, 536, 543, 600, 644, 665, 666, 667, 675, 676, 677, 798, 799, 806, 840, 841, 842, 843, 844, 862, 863, 989, 1010, 1039], "consensu": [2, 59, 60, 73, 370, 371, 386, 400, 403, 648, 670, 677, 678, 719, 1000], "consensus_scor": [2, 59, 60, 400, 1033], "consequ": [90, 127, 223, 263, 264, 303, 321, 332, 354, 401, 402, 408, 410, 558, 559, 561, 562, 563, 654, 655, 990, 1000, 1008, 1016, 1040, 1042, 1047, 1049], "conserv": [51, 375, 385, 580, 587, 897, 898, 999, 1012], "consid": [0, 44, 52, 53, 54, 59, 63, 64, 88, 99, 100, 103, 112, 120, 124, 126, 139, 141, 143, 146, 151, 154, 155, 156, 158, 162, 163, 179, 183, 184, 185, 200, 206, 208, 233, 234, 239, 244, 246, 257, 262, 263, 264, 266, 270, 272, 274, 275, 277, 283, 286, 289, 290, 301, 303, 309, 313, 314, 316, 317, 319, 320, 322, 326, 332, 339, 340, 342, 346, 352, 353, 354, 358, 359, 360, 363, 370, 371, 373, 377, 379, 383, 385, 386, 394, 397, 399, 402, 403, 408, 409, 410, 411, 412, 413, 414, 415, 434, 440, 442, 446, 453, 470, 505, 506, 518, 530, 538, 546, 547, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 583, 584, 586, 587, 589, 591, 605, 606, 619, 630, 631, 632, 645, 651, 657, 658, 659, 661, 670, 678, 688, 689, 690, 692, 693, 694, 696, 698, 700, 702, 705, 707, 715, 727, 731, 735, 757, 787, 789, 795, 847, 848, 849, 851, 853, 855, 856, 857, 860, 862, 863, 868, 878, 879, 886, 900, 901, 910, 911, 913, 914, 915, 916, 926, 958, 983, 989, 995, 996, 997, 998, 1000, 1001, 1003, 1006, 1007, 1008, 1010, 1014, 1015, 1016, 1028, 1034, 1035, 1039, 1043, 1044, 1046, 1047, 1048], "consider": [103, 104, 148, 149, 168, 171, 242, 244, 253, 258, 261, 262, 263, 264, 270, 275, 366, 371, 402, 413, 618, 801, 804, 805, 815, 823, 989, 996, 1002, 1006, 1014, 1024, 1048], "consist": [0, 2, 44, 47, 64, 69, 73, 89, 90, 102, 103, 104, 111, 118, 120, 140, 143, 149, 150, 154, 156, 160, 162, 163, 170, 172, 175, 179, 183, 184, 185, 186, 200, 206, 223, 234, 238, 242, 244, 269, 270, 272, 300, 308, 309, 312, 313, 315, 316, 342, 347, 354, 358, 364, 366, 368, 371, 373, 377, 378, 379, 380, 384, 385, 386, 399, 401, 403, 405, 409, 410, 411, 421, 422, 425, 426, 436, 437, 438, 439, 440, 441, 443, 444, 445, 446, 447, 448, 449, 459, 460, 461, 463, 465, 466, 467, 468, 469, 470, 471, 472, 478, 479, 480, 486, 494, 528, 530, 531, 532, 533, 534, 535, 536, 537, 539, 540, 541, 551, 553, 555, 557, 559, 560, 562, 563, 564, 565, 566, 567, 579, 580, 581, 586, 587, 589, 610, 626, 627, 628, 629, 634, 637, 642, 643, 644, 645, 646, 648, 649, 650, 651, 652, 653, 654, 655, 656, 659, 660, 661, 662, 663, 664, 666, 669, 671, 672, 673, 676, 677, 678, 686, 687, 688, 689, 690, 691, 700, 736, 798, 799, 808, 833, 834, 835, 837, 838, 839, 840, 848, 849, 851, 853, 856, 857, 863, 868, 869, 870, 872, 876, 877, 880, 881, 883, 884, 886, 897, 898, 901, 905, 906, 908, 909, 911, 914, 916, 922, 931, 933, 956, 970, 973, 988, 989, 992, 993, 994, 996, 997, 999, 1000, 1001, 1003, 1004, 1010, 1013, 1015, 1016, 1020, 1024, 1031, 1032, 1033, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052], "consol": 142, "consolid": [0, 385, 386, 1031], "consolidate_scor": 53, "consortium": [0, 1024], "constant": [2, 44, 103, 129, 137, 139, 149, 154, 155, 170, 173, 174, 179, 183, 185, 196, 207, 208, 210, 223, 233, 234, 239, 243, 244, 246, 260, 262, 266, 277, 295, 299, 301, 304, 306, 313, 316, 322, 342, 344, 354, 363, 373, 380, 385, 400, 401, 410, 413, 426, 442, 461, 478, 479, 480, 508, 510, 531, 535, 537, 544, 546, 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995, 996, 1001, 1003, 1005, 1020, 1031, 1034], "direction": 1049, "direction_vector": 301, "directionfor": 52, "directli": [0, 46, 58, 65, 80, 89, 103, 104, 126, 139, 143, 154, 155, 160, 163, 172, 175, 183, 185, 194, 196, 206, 212, 223, 233, 234, 243, 244, 246, 253, 257, 261, 262, 263, 264, 268, 270, 275, 277, 301, 303, 309, 313, 316, 319, 320, 321, 322, 348, 354, 358, 369, 371, 372, 373, 375, 377, 383, 384, 385, 386, 399, 400, 401, 403, 404, 405, 406, 407, 411, 413, 438, 448, 458, 460, 461, 465, 470, 524, 564, 565, 566, 567, 591, 595, 610, 619, 631, 642, 645, 646, 651, 652, 659, 660, 661, 662, 676, 680, 683, 690, 731, 775, 779, 782, 793, 794, 801, 804, 805, 815, 829, 830, 832, 848, 850, 864, 865, 866, 872, 882, 884, 887, 894, 989, 996, 998, 1003, 1005, 1007, 1010, 1015, 1016, 1028, 1031, 1039, 1040, 1042, 1043, 1044, 1047, 1048, 1050, 1051, 1052], "director": 1024, "directori": [0, 2, 48, 103, 139, 143, 154, 160, 183, 185, 233, 234, 243, 244, 246, 264, 270, 277, 285, 309, 313, 316, 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111, 112, 113, 126, 127, 128, 158, 160, 163, 180, 188, 220, 226, 240, 253, 292, 368, 401, 410, 471, 509, 546, 547, 563, 630, 784, 854, 989, 997, 1000, 1003, 1014, 1016, 1021, 1022, 1026, 1034], "discriminant_analysi": [2, 68, 70, 71, 128, 226, 292, 354, 399, 533, 546, 547, 854, 994, 1001, 1031, 1034, 1035, 1036, 1037, 1038], "discriminatori": 401, "discuss": [44, 86, 91, 163, 183, 198, 225, 230, 257, 266, 280, 346, 354, 360, 366, 368, 371, 376, 377, 379, 383, 385, 386, 402, 403, 405, 409, 410, 412, 413, 495, 681, 682, 847, 848, 853, 855, 856, 990, 994, 996, 997, 1000, 1003, 1010, 1017, 1018, 1020, 1023, 1034], "diseas": [47, 156, 163, 179, 266, 368, 712, 1000], "disentangl": 997, "disjoint": [143, 401, 403, 404, 433, 704, 732, 756, 758, 997], "disk": [46, 54, 321, 366, 371, 376, 385, 397], "disp": [44, 67, 69, 136, 149, 150, 151, 155, 256, 266, 276, 286, 308, 313, 319, 332, 337, 434, 630, 697, 700, 701, 854], "disp1": 631, "disp2": 631, "dispar": [690, 694, 997], "dispatch": [103, 104, 244, 253, 257, 261, 262, 263, 385, 399, 464, 801, 815, 826, 827, 828, 903, 967, 968, 1026, 1046, 1049, 1050], "dispatch_next": 967, "dispatch_one_batch": 967, "dispatchedthan": [103, 104, 244, 253, 257, 261, 262, 263], "dispers": [71, 277, 403, 710, 726, 996], "displai": [2, 52, 63, 65, 67, 71, 73, 80, 93, 103, 104, 112, 113, 115, 116, 120, 140, 150, 151, 154, 178, 180, 183, 184, 197, 223, 231, 232, 242, 246, 252, 253, 257, 260, 261, 262, 263, 264, 265, 270, 272, 276, 278, 293, 309, 313, 314, 334, 351, 358, 371, 373, 374, 378, 380, 389, 391, 404, 408, 411, 412, 417, 434, 460, 463, 464, 493, 538, 561, 629, 630, 631, 657, 686, 697, 698, 700, 701, 702, 713, 718, 783, 790, 801, 807, 815, 824, 831, 865, 866, 878, 880, 885, 903, 910, 917, 918, 919, 939, 967, 986, 1000, 1004, 1019, 1021, 1026, 1040, 1042, 1043, 1045, 1046, 1052], "display_label": [46, 256, 697, 1043], "dispos": 989, "disproportion": 409, "disput": 140, "disregard": [346, 385, 411, 426, 461, 478, 479, 480, 549, 551, 553, 555, 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914, 915, 916, 1000, 1032, 1039, 1040, 1047], "expand_frame_repr": 223, "expans": [44, 178, 314, 446, 880, 1040], "expect": [2, 44, 45, 50, 53, 62, 64, 67, 73, 79, 86, 103, 104, 116, 118, 125, 134, 137, 139, 141, 143, 146, 149, 154, 156, 160, 161, 167, 173, 180, 181, 183, 185, 195, 199, 206, 207, 208, 210, 213, 223, 234, 235, 236, 239, 242, 243, 244, 249, 250, 253, 254, 257, 262, 264, 269, 270, 283, 308, 309, 313, 316, 321, 342, 344, 346, 347, 354, 358, 359, 371, 373, 375, 376, 377, 378, 379, 380, 383, 384, 385, 386, 388, 394, 399, 401, 403, 404, 405, 407, 410, 411, 412, 414, 426, 460, 461, 463, 464, 465, 478, 479, 480, 493, 520, 529, 533, 535, 538, 549, 551, 552, 553, 555, 557, 559, 560, 562, 564, 565, 567, 569, 572, 586, 587, 589, 593, 595, 604, 610, 626, 631, 632, 634, 642, 643, 644, 645, 646, 648, 649, 650, 651, 652, 653, 654, 655, 656, 659, 660, 661, 662, 663, 664, 666, 669, 671, 672, 673, 674, 677, 678, 695, 704, 705, 716, 736, 789, 795, 801, 815, 838, 839, 840, 841, 842, 843, 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411, 989, 999, 1001, 1016, 1019, 1024, 1034], "explic": 1012, "explicit": [44, 143, 149, 180, 184, 188, 231, 239, 277, 339, 358, 359, 360, 370, 372, 383, 385, 386, 394, 404, 411, 417, 468, 496, 498, 499, 538, 548, 549, 637, 638, 640, 675, 801, 802, 809, 849, 857, 865, 905, 910, 965, 992, 994, 996, 1000, 1010, 1016, 1019, 1021, 1031, 1035, 1040, 1041, 1044, 1048, 1051], "explicitli": [44, 80, 103, 139, 141, 143, 155, 167, 174, 178, 179, 207, 233, 234, 235, 239, 244, 246, 257, 262, 275, 277, 301, 313, 316, 319, 322, 339, 348, 354, 359, 365, 367, 371, 372, 373, 375, 383, 385, 394, 397, 399, 407, 411, 413, 490, 532, 546, 547, 577, 578, 595, 626, 647, 657, 658, 668, 679, 709, 722, 728, 783, 789, 790, 804, 805, 836, 839, 845, 846, 871, 895, 896, 988, 989, 990, 992, 994, 996, 997, 1000, 1002, 1003, 1010, 1024, 1031, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1043, 1044, 1045, 1049, 1050], "explicitvalu": 52, "explod": 309, "exploit": [63, 282, 397, 1001, 1020, 1034], 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1014, 1015, 1031, 1038, 1043, 1045, 1048], "intercept_hidden_": 861, "intercept_init": [665, 666, 667, 675, 677], "intercept_sc": [103, 104, 139, 155, 160, 233, 234, 244, 246, 257, 262, 270, 277, 301, 313, 316, 322, 657, 658, 905, 906, 912, 996, 1015], "intercept_visible_": 861, "interceptparamet": 64, "intercepts_": [862, 863, 1004], "interchang": [1019, 1048], "interclass": 75, "interdepend": 512, "interest": [44, 46, 53, 67, 77, 79, 83, 88, 95, 99, 104, 134, 146, 154, 160, 163, 172, 178, 183, 184, 185, 198, 206, 207, 208, 223, 226, 237, 256, 257, 260, 263, 264, 266, 270, 272, 277, 280, 320, 321, 335, 342, 347, 353, 358, 366, 368, 371, 373, 376, 377, 379, 383, 386, 402, 403, 405, 408, 411, 413, 490, 491, 497, 501, 507, 645, 646, 905, 990, 996, 997, 1000, 1005, 1006, 1007, 1020, 1024, 1031, 1048], "interestingli": [109, 163, 1010], "interfac": [2, 43, 103, 104, 134, 139, 143, 154, 160, 180, 183, 185, 233, 234, 243, 244, 246, 253, 257, 261, 262, 263, 264, 270, 275, 277, 309, 313, 316, 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"sklearn.kernel_ridge", "sklearn.linear_model", "sklearn.manifold", "sklearn.metrics", "sklearn.mixture", "sklearn.model_selection", "sklearn.multiclass", "sklearn.multioutput", "sklearn.naive_bayes", "sklearn.neighbors", "sklearn.neural_network", "sklearn.pipeline", "sklearn.preprocessing", "sklearn.random_projection", "sklearn.semi_supervised", "sklearn.svm", "sklearn.tree", "sklearn.utils", "Examples based on real world datasets", "Time-related feature engineering", "Image denoising using kernel PCA", "Faces recognition example using eigenfaces and SVMs", "Model Complexity Influence", "Out-of-core classification of text documents", "Outlier detection on a real data set", "Prediction Latency", "Species distribution modeling", "Visualizing the stock market structure", "Lagged features for time series forecasting", "Compressive sensing: tomography reconstruction with L1 prior (Lasso)", "Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation", "Wikipedia principal eigenvector", "Biclustering", "Biclustering documents with the Spectral Co-clustering algorithm", "A demo of the Spectral Biclustering algorithm", "A demo of the Spectral Co-Clustering algorithm", "Calibration", "Probability calibration of classifiers", "Probability Calibration curves", "Probability Calibration for 3-class classification", "Comparison of Calibration of Classifiers", "Classification", "Plot classification probability", "Classifier comparison", "Recognizing hand-written digits", "Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification", "Linear and Quadratic Discriminant Analysis with covariance ellipsoid", "Clustering", "Adjustment for chance in clustering performance evaluation", "Demo of affinity propagation clustering algorithm", "Agglomerative clustering with different metrics", "Plot Hierarchical Clustering Dendrogram", "Compare BIRCH and MiniBatchKMeans", "Bisecting K-Means and Regular K-Means Performance Comparison", "Comparing different clustering algorithms on toy datasets", "Segmenting the picture of greek coins in regions", "A demo of structured Ward hierarchical clustering on an image of coins", "Demo of DBSCAN clustering algorithm", "Online learning of a dictionary of parts of faces", "Feature agglomeration", "Various Agglomerative Clustering on a 2D embedding of digits", "Vector Quantization Example", "Feature agglomeration vs. univariate selection", "Demo of HDBSCAN clustering algorithm", "Inductive Clustering", "Demonstration of k-means assumptions", "A demo of K-Means clustering on the handwritten digits data", "An example of K-Means++ initialization", "Selecting the number of clusters with silhouette analysis on KMeans clustering", "Empirical evaluation of the impact of k-means initialization", "Comparing different hierarchical linkage methods on toy datasets", "A demo of the mean-shift clustering algorithm", "Comparison of the K-Means and MiniBatchKMeans clustering algorithms", "Demo of OPTICS clustering algorithm", "Spectral clustering for image segmentation", "Hierarchical clustering with and without structure", "Pipelines and composite estimators", "Column Transformer with Heterogeneous Data Sources", "Column Transformer with Mixed Types", "Selecting dimensionality reduction with Pipeline and GridSearchCV", "Pipelining: chaining a PCA and a logistic regression", "Concatenating multiple feature extraction methods", "Effect of transforming the targets in regression model", "Covariance estimation", "Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood", "Ledoit-Wolf vs OAS estimation", "Robust covariance estimation and Mahalanobis distances relevance", "Robust vs Empirical covariance estimate", "Sparse inverse covariance estimation", "Cross decomposition", "Compare cross decomposition methods", "Principal Component Regression vs Partial Least Squares Regression", "Dataset examples", "Plot randomly generated multilabel dataset", "Decomposition", "Faces dataset decompositions", "Blind source separation using FastICA", "FastICA on 2D point clouds", "Image denoising using dictionary learning", "Incremental PCA", "Kernel PCA", "Principal Component Analysis (PCA) on Iris Dataset", "Model selection with Probabilistic PCA and Factor Analysis (FA)", "Comparison of LDA and PCA 2D projection of Iris dataset", "Sparse coding with a precomputed dictionary", "Factor Analysis (with rotation) to visualize patterns", "Developing Estimators", "<code class=\"docutils literal notranslate\"><span class=\"pre\">__sklearn_is_fitted__</span></code> as Developer API", "Ensemble methods", "Multi-class AdaBoosted Decision Trees", "Decision Tree Regression with AdaBoost", "Two-class AdaBoost", "Single estimator versus bagging: bias-variance decomposition", "OOB Errors for Random Forests", "Feature transformations with ensembles of trees", "Comparing Random Forests and Histogram Gradient Boosting models", "Feature importances with a forest of trees", "Plot the decision surfaces of ensembles of trees on the iris dataset", "Categorical Feature Support in Gradient Boosting", "Early stopping in Gradient Boosting", "Gradient Boosting Out-of-Bag estimates", "Prediction Intervals for Gradient Boosting Regression", "Gradient Boosting regression", "Gradient Boosting regularization", "Features in Histogram Gradient Boosting Trees", "IsolationForest example", "Monotonic Constraints", "Hashing feature transformation using Totally Random Trees", "Comparing random forests and the multi-output meta estimator", "Combine predictors using stacking", "Visualizing the probabilistic predictions of a VotingClassifier", "Plot individual and voting regression predictions", "Feature Selection", "Comparison of F-test and mutual information", "Univariate Feature Selection", "Pipeline ANOVA SVM", "Recursive feature elimination", "Recursive feature elimination with cross-validation", "Model-based and sequential feature selection", "Frozen Estimators", "Examples of Using <code class=\"docutils literal notranslate\"><span class=\"pre\">FrozenEstimator</span></code>", "Gaussian Process for Machine Learning", "Comparison of kernel ridge and Gaussian process regression", "Probabilistic predictions with Gaussian process classification (GPC)", "Gaussian process classification (GPC) on iris dataset", "Iso-probability lines for Gaussian Processes classification (GPC)", "Illustration of Gaussian process classification (GPC) on the XOR dataset", "Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)", "Ability of Gaussian process regression (GPR) to estimate data noise-level", "Gaussian Processes regression: basic introductory example", "Gaussian processes on discrete data structures", "Illustration of prior and posterior Gaussian process for different kernels", "Missing Value Imputation", "Imputing missing values with variants of IterativeImputer", "Imputing missing values before building an estimator", "Examples", "Inspection", "Failure of Machine Learning to infer causal effects", "Common pitfalls in the interpretation of coefficients of linear models", "Partial Dependence and Individual Conditional Expectation Plots", "Permutation Importance vs Random Forest Feature Importance (MDI)", "Permutation Importance with Multicollinear or Correlated Features", "Kernel Approximation", "Scalable learning with polynomial kernel approximation", "Generalized Linear Models", "Comparing Linear Bayesian Regressors", "Curve Fitting with Bayesian Ridge Regression", "Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples", "HuberRegressor vs Ridge on dataset with strong outliers", "L1-based models for Sparse Signals", "Lasso on dense and sparse data", "Lasso model selection via information criteria", "Lasso, Lasso-LARS, and Elastic Net paths", "Lasso model selection: AIC-BIC / cross-validation", "L1 Penalty and Sparsity in Logistic Regression", "Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression", "Regularization path of L1- Logistic Regression", "Joint feature selection with multi-task Lasso", "Non-negative least squares", "Ordinary Least Squares and Ridge Regression", "Orthogonal Matching Pursuit", "Poisson regression and non-normal loss", "Polynomial and Spline interpolation", "Quantile regression", "Robust linear model estimation using RANSAC", "Ridge coefficients as a function of the L2 Regularization", "Plot Ridge coefficients as a function of the regularization", "Robust linear estimator fitting", "Early stopping of Stochastic Gradient Descent", "Plot multi-class SGD on the iris dataset", "SGD: convex loss functions", "SGD: Penalties", "SGD: Maximum margin separating hyperplane", "SGD: Weighted samples", "One-Class SVM versus One-Class SVM using Stochastic Gradient Descent", "Multiclass sparse logistic regression on 20newgroups", "MNIST classification using multinomial logistic + L1", "Theil-Sen Regression", "Tweedie regression on insurance claims", "Manifold learning", "Comparison of Manifold Learning methods", "Manifold learning on handwritten digits: Locally Linear Embedding, Isomap\u2026", "Manifold Learning methods on a severed sphere", "Multi-dimensional scaling", "Swiss Roll And Swiss-Hole Reduction", "t-SNE: The effect of various perplexity values on the shape", "Miscellaneous", "Comparing anomaly detection algorithms for outlier detection on toy datasets", "Visualizations with Display Objects", "Displaying estimators and complex pipelines", "Isotonic Regression", "The Johnson-Lindenstrauss bound for embedding with random projections", "Explicit feature map approximation for RBF kernels", "Comparison of kernel ridge regression and SVR", "Metadata Routing", "Multilabel classification", "Face completion with a multi-output estimators", "Evaluation of outlier detection estimators", "Advanced Plotting With Partial Dependence", "Displaying Pipelines", "ROC Curve with Visualization API", "Introducing the <code class=\"docutils literal notranslate\"><span class=\"pre\">set_output</span></code> API", "Gaussian Mixture Models", "Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture", "Gaussian Mixture Model Ellipsoids", "GMM covariances", "GMM Initialization Methods", "Density Estimation for a Gaussian mixture", "Gaussian Mixture Model Selection", "Gaussian Mixture Model Sine Curve", "Model Selection", "Confusion matrix", "Post-tuning the decision threshold for cost-sensitive learning", "Visualizing cross-validation behavior in scikit-learn", "Plotting Cross-Validated Predictions", "Detection error tradeoff (DET) curve", "Custom refit strategy of a grid search with cross-validation", "Balance model complexity and cross-validated score", "Statistical comparison of models using grid search", "Sample pipeline for text feature extraction and evaluation", "Plotting Learning Curves and Checking Models\u2019 Scalability", "Class Likelihood Ratios to measure classification performance", "Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV", "Nested versus non-nested cross-validation", "Test with permutations the significance of a classification score", "Precision-Recall", "Comparing randomized search and grid search for hyperparameter estimation", "Multiclass Receiver Operating Characteristic (ROC)", "Receiver Operating Characteristic (ROC) with cross validation", "Comparison between grid search and successive halving", "Successive Halving Iterations", "Effect of model regularization on training and test error", "Post-hoc tuning the cut-off point of decision function", "Underfitting vs. Overfitting", "Multiclass methods", "Overview of multiclass training meta-estimators", "Multioutput methods", "Multilabel classification using a classifier chain", "Approximate nearest neighbors in TSNE", "Nearest Neighbors", "Caching nearest neighbors", "Nearest Neighbors Classification", "Kernel Density Estimation", "Simple 1D Kernel Density Estimation", "Novelty detection with Local Outlier Factor (LOF)", "Outlier detection with Local Outlier Factor (LOF)", "Comparing Nearest Neighbors with and without Neighborhood Components Analysis", "Dimensionality Reduction with Neighborhood Components Analysis", "Neighborhood Components Analysis Illustration", "Nearest Centroid Classification", "Nearest Neighbors regression", "Kernel Density Estimate of Species Distributions", "Neural Networks", "Varying regularization in Multi-layer Perceptron", "Compare Stochastic learning strategies for MLPClassifier", "Visualization of MLP weights on MNIST", "Restricted Boltzmann Machine features for digit classification", "Preprocessing", "Compare the effect of different scalers on data with outliers", "Using KBinsDiscretizer to discretize continuous features", "Feature discretization", "Demonstrating the different strategies of KBinsDiscretizer", "Map data to a normal distribution", "Importance of Feature Scaling", "Comparing Target Encoder with Other Encoders", "Target Encoder\u2019s Internal Cross fitting", "Release Highlights", "Release Highlights for scikit-learn 0.22", "Release Highlights for scikit-learn 0.23", "Release Highlights for scikit-learn 0.24", "Release Highlights for scikit-learn 1.0", "Release Highlights for scikit-learn 1.1", "Release Highlights for scikit-learn 1.2", "Release Highlights for scikit-learn 1.3", "Release Highlights for scikit-learn 1.4", "Release Highlights for scikit-learn 1.5", "Release Highlights for scikit-learn 1.6", "Release Highlights for scikit-learn 1.7", "Semi Supervised Classification", "Label Propagation digits: Demonstrating performance", "Label Propagation digits: Active learning", "Label Propagation circles: Learning a complex structure", "Effect of varying threshold for self-training", "Semi-supervised Classification on a Text Dataset", "Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset", "Support Vector Machines", "SVM with custom kernel", "Plot different SVM classifiers in the iris dataset", "Plot the support vectors in LinearSVC", "One-class SVM with non-linear kernel (RBF)", "RBF SVM parameters", "SVM: Maximum margin separating hyperplane", "SVM: Separating hyperplane for unbalanced classes", "SVM-Anova: SVM with univariate feature selection", "Plot classification boundaries with different SVM Kernels", "SVM Margins Example", "Support Vector Regression (SVR) using linear and non-linear kernels", "Scaling the regularization parameter for SVCs", "SVM Tie Breaking Example", "SVM: Weighted samples", "Working with text documents", "Classification of text documents using sparse features", "Clustering text documents using k-means", "FeatureHasher and DictVectorizer Comparison", "Decision Trees", "Post pruning decision trees with cost complexity pruning", "Plot the decision surface of decision trees trained on the iris dataset", "Decision Tree Regression", "Understanding the decision tree structure", "<span class=\"section-number\">11. </span>Common pitfalls and recommended practices", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">9. </span>Computing with scikit-learn", "<span class=\"section-number\">9.2. </span>Computational Performance", "<span class=\"section-number\">9.3. </span>Parallelism, resource management, and configuration", "<span class=\"section-number\">9.1. </span>Strategies to scale computationally: bigger data", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">7. </span>Dataset transformations", "<span class=\"section-number\">8. </span>Dataset loading utilities", "<span class=\"section-number\">8.4. </span>Loading other datasets", "<span class=\"section-number\">8.2. </span>Real world datasets", "<span class=\"section-number\">8.3. </span>Generated datasets", "<span class=\"section-number\">8.1. </span>Toy datasets", "Installing the development version of scikit-learn", "Bug triaging and issue curation", "Contributing", "Cython Best Practices, Conventions and Knowledge", "Developing scikit-learn estimators", "Developer\u2019s Guide", "Maintainer Information", "Crafting a minimal reproducer for scikit-learn", "How to optimize for speed", "Developing with the Plotting API", "Developers\u2019 Tips and Tricks", "Utilities for Developers", "<span class=\"section-number\">12. </span>Dispatching", "&lt;no title&gt;", "Frequently Asked Questions", "Getting Started", "Glossary of Common Terms and API Elements", "Scikit-learn governance and decision-making", "Index", "<span class=\"section-number\">5. </span>Inspection", "Installing scikit-learn", "&lt;no title&gt;", "<span class=\"section-number\">13. </span>Choosing the right estimator", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">4. </span>Metadata Routing", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">10. </span>Model persistence", "<span class=\"section-number\">3. </span>Model selection and evaluation", "<span class=\"section-number\">12.1. </span>Array API support (experimental)", "<span class=\"section-number\">2.4. </span>Biclustering", "<span class=\"section-number\">1.16. </span>Probability calibration", "<span class=\"section-number\">3.3. </span>Tuning the decision threshold for class prediction", "<span class=\"section-number\">2.3. </span>Clustering", "<span class=\"section-number\">7.1. </span>Pipelines and composite estimators", "<span class=\"section-number\">2.6. </span>Covariance estimation", "<span class=\"section-number\">1.8. </span>Cross decomposition", "<span class=\"section-number\">3.1. </span>Cross-validation: evaluating estimator performance", "<span class=\"section-number\">2.5. </span>Decomposing signals in components (matrix factorization problems)", "<span class=\"section-number\">2.8. </span>Density Estimation", "<span class=\"section-number\">1.11. </span>Ensembles: Gradient boosting, random forests, bagging, voting, stacking", "<span class=\"section-number\">7.2. </span>Feature extraction", "<span class=\"section-number\">1.13. </span>Feature selection", "<span class=\"section-number\">1.7. </span>Gaussian Processes", "dbscan", "fastica", "oas", "BaseEstimator", "BiclusterMixin", "ClassNamePrefixFeaturesOutMixin", "ClassifierMixin", "ClusterMixin", "DensityMixin", "MetaEstimatorMixin", "OneToOneFeatureMixin", "OutlierMixin", "RegressorMixin", "TransformerMixin", "clone", "is_classifier", "is_clusterer", "is_outlier_detector", "is_regressor", "CalibratedClassifierCV", "CalibrationDisplay", "calibration_curve", "AffinityPropagation", "AgglomerativeClustering", "Birch", "BisectingKMeans", "DBSCAN", "FeatureAgglomeration", "HDBSCAN", "KMeans", "MeanShift", "MiniBatchKMeans", "OPTICS", "SpectralBiclustering", "SpectralClustering", "SpectralCoclustering", "affinity_propagation", "cluster_optics_dbscan", "cluster_optics_xi", "compute_optics_graph", "estimate_bandwidth", "k_means", "kmeans_plusplus", "mean_shift", "spectral_clustering", "ward_tree", "ColumnTransformer", "TransformedTargetRegressor", "make_column_selector", "make_column_transformer", "config_context", "EllipticEnvelope", "EmpiricalCovariance", "GraphicalLasso", "GraphicalLassoCV", "LedoitWolf", "MinCovDet", "OAS", "ShrunkCovariance", "empirical_covariance", "graphical_lasso", "ledoit_wolf", "ledoit_wolf_shrinkage", "shrunk_covariance", "CCA", "PLSCanonical", "PLSRegression", "PLSSVD", "clear_data_home", "dump_svmlight_file", "fetch_20newsgroups", "fetch_20newsgroups_vectorized", "fetch_california_housing", "fetch_covtype", "fetch_file", "fetch_kddcup99", "fetch_lfw_pairs", "fetch_lfw_people", "fetch_olivetti_faces", "fetch_openml", "fetch_rcv1", "fetch_species_distributions", "get_data_home", "load_breast_cancer", "load_diabetes", "load_digits", "load_files", "load_iris", "load_linnerud", "load_sample_image", "load_sample_images", "load_svmlight_file", "load_svmlight_files", "load_wine", "make_biclusters", "make_blobs", "make_checkerboard", "make_circles", "make_classification", "make_friedman1", "make_friedman2", "make_friedman3", "make_gaussian_quantiles", "make_hastie_10_2", "make_low_rank_matrix", "make_moons", "make_multilabel_classification", "make_regression", "make_s_curve", "make_sparse_coded_signal", "make_sparse_spd_matrix", "make_sparse_uncorrelated", "make_spd_matrix", "make_swiss_roll", "DictionaryLearning", "FactorAnalysis", "FastICA", "IncrementalPCA", "KernelPCA", "LatentDirichletAllocation", "MiniBatchDictionaryLearning", "MiniBatchNMF", "MiniBatchSparsePCA", "NMF", "PCA", "SparseCoder", "SparsePCA", "TruncatedSVD", "dict_learning", "dict_learning_online", "non_negative_factorization", "sparse_encode", "LinearDiscriminantAnalysis", "QuadraticDiscriminantAnalysis", "DummyClassifier", "DummyRegressor", "AdaBoostClassifier", "AdaBoostRegressor", "BaggingClassifier", "BaggingRegressor", "ExtraTreesClassifier", "ExtraTreesRegressor", "GradientBoostingClassifier", "GradientBoostingRegressor", "HistGradientBoostingClassifier", "HistGradientBoostingRegressor", "IsolationForest", "RandomForestClassifier", "RandomForestRegressor", "RandomTreesEmbedding", "StackingClassifier", "StackingRegressor", "VotingClassifier", "VotingRegressor", "ConvergenceWarning", "DataConversionWarning", "DataDimensionalityWarning", "EfficiencyWarning", "EstimatorCheckFailedWarning", "FitFailedWarning", "InconsistentVersionWarning", "NotFittedError", "UndefinedMetricWarning", "enable_halving_search_cv", "enable_iterative_imputer", "DictVectorizer", "FeatureHasher", "PatchExtractor", "extract_patches_2d", "grid_to_graph", "img_to_graph", "reconstruct_from_patches_2d", "CountVectorizer", "HashingVectorizer", "TfidfTransformer", "TfidfVectorizer", "GenericUnivariateSelect", "RFE", "RFECV", "SelectFdr", "SelectFpr", "SelectFromModel", "SelectFwe", "SelectKBest", "SelectPercentile", "SelectorMixin", "SequentialFeatureSelector", "VarianceThreshold", "chi2", "f_classif", "f_regression", "mutual_info_classif", "mutual_info_regression", "r_regression", "FrozenEstimator", "GaussianProcessClassifier", "GaussianProcessRegressor", "CompoundKernel", "ConstantKernel", "DotProduct", "ExpSineSquared", "Exponentiation", "Hyperparameter", "Kernel", "Matern", "PairwiseKernel", "Product", "RBF", "RationalQuadratic", "Sum", "WhiteKernel", "get_config", "IterativeImputer", "KNNImputer", "MissingIndicator", "SimpleImputer", "DecisionBoundaryDisplay", "PartialDependenceDisplay", "partial_dependence", "permutation_importance", "IsotonicRegression", "check_increasing", "isotonic_regression", "AdditiveChi2Sampler", "Nystroem", "PolynomialCountSketch", "RBFSampler", "SkewedChi2Sampler", "KernelRidge", "ARDRegression", "BayesianRidge", "ElasticNet", "ElasticNetCV", "GammaRegressor", "HuberRegressor", "Lars", "LarsCV", "Lasso", "LassoCV", "LassoLars", "LassoLarsCV", "LassoLarsIC", "LinearRegression", "LogisticRegression", "LogisticRegressionCV", "MultiTaskElasticNet", "MultiTaskElasticNetCV", "MultiTaskLasso", "MultiTaskLassoCV", "OrthogonalMatchingPursuit", "OrthogonalMatchingPursuitCV", "PassiveAggressiveClassifier", "PassiveAggressiveRegressor", "Perceptron", "PoissonRegressor", "QuantileRegressor", "RANSACRegressor", "Ridge", "RidgeCV", "RidgeClassifier", "RidgeClassifierCV", "SGDClassifier", "SGDOneClassSVM", "SGDRegressor", "TheilSenRegressor", "TweedieRegressor", "enet_path", "lars_path", "lars_path_gram", "lasso_path", "orthogonal_mp", "orthogonal_mp_gram", "ridge_regression", "ClassicalMDS", "Isomap", "LocallyLinearEmbedding", "MDS", "SpectralEmbedding", "TSNE", "locally_linear_embedding", "smacof", "spectral_embedding", "trustworthiness", "ConfusionMatrixDisplay", "DetCurveDisplay", "DistanceMetric", "PrecisionRecallDisplay", "PredictionErrorDisplay", "RocCurveDisplay", "accuracy_score", "adjusted_mutual_info_score", "adjusted_rand_score", "auc", "average_precision_score", "balanced_accuracy_score", "brier_score_loss", "calinski_harabasz_score", "check_scoring", "class_likelihood_ratios", "classification_report", "contingency_matrix", "pair_confusion_matrix", "cohen_kappa_score", "completeness_score", "confusion_matrix", "consensus_score", "coverage_error", "d2_absolute_error_score", "d2_brier_score", "d2_log_loss_score", "d2_pinball_score", "d2_tweedie_score", "davies_bouldin_score", "dcg_score", "det_curve", "explained_variance_score", "f1_score", "fbeta_score", "fowlkes_mallows_score", "get_scorer", "get_scorer_names", "hamming_loss", "hinge_loss", "homogeneity_completeness_v_measure", "homogeneity_score", "jaccard_score", "label_ranking_average_precision_score", "label_ranking_loss", "log_loss", "make_scorer", "matthews_corrcoef", "max_error", "mean_absolute_error", "mean_absolute_percentage_error", "mean_gamma_deviance", "mean_pinball_loss", "mean_poisson_deviance", "mean_squared_error", "mean_squared_log_error", "mean_tweedie_deviance", "median_absolute_error", "multilabel_confusion_matrix", "mutual_info_score", "ndcg_score", "normalized_mutual_info_score", "additive_chi2_kernel", "chi2_kernel", "cosine_distances", "cosine_similarity", "distance_metrics", "euclidean_distances", "haversine_distances", "kernel_metrics", "laplacian_kernel", "linear_kernel", "manhattan_distances", "nan_euclidean_distances", "paired_cosine_distances", "paired_distances", "paired_euclidean_distances", "paired_manhattan_distances", "pairwise_kernels", "polynomial_kernel", "rbf_kernel", "sigmoid_kernel", "pairwise_distances", "pairwise_distances_argmin", "pairwise_distances_argmin_min", "pairwise_distances_chunked", "precision_recall_curve", "precision_recall_fscore_support", "precision_score", "r2_score", "rand_score", "recall_score", "roc_auc_score", "roc_curve", "root_mean_squared_error", "root_mean_squared_log_error", "silhouette_samples", "silhouette_score", "top_k_accuracy_score", "v_measure_score", "zero_one_loss", "BayesianGaussianMixture", "GaussianMixture", "FixedThresholdClassifier", "GridSearchCV", "GroupKFold", "GroupShuffleSplit", "HalvingGridSearchCV", "HalvingRandomSearchCV", "KFold", "LearningCurveDisplay", "LeaveOneGroupOut", "LeaveOneOut", "LeavePGroupsOut", "LeavePOut", "ParameterGrid", "ParameterSampler", "PredefinedSplit", "RandomizedSearchCV", "RepeatedKFold", "RepeatedStratifiedKFold", "ShuffleSplit", "StratifiedGroupKFold", "StratifiedKFold", "StratifiedShuffleSplit", "TimeSeriesSplit", "TunedThresholdClassifierCV", "ValidationCurveDisplay", "check_cv", "cross_val_predict", "cross_val_score", "cross_validate", "learning_curve", "permutation_test_score", "train_test_split", "validation_curve", "OneVsOneClassifier", "OneVsRestClassifier", "OutputCodeClassifier", "ClassifierChain", "MultiOutputClassifier", "MultiOutputRegressor", "RegressorChain", "BernoulliNB", "CategoricalNB", "ComplementNB", "GaussianNB", "MultinomialNB", "BallTree", "KDTree", "KNeighborsClassifier", "KNeighborsRegressor", "KNeighborsTransformer", "KernelDensity", "LocalOutlierFactor", "NearestCentroid", "NearestNeighbors", "NeighborhoodComponentsAnalysis", "RadiusNeighborsClassifier", "RadiusNeighborsRegressor", "RadiusNeighborsTransformer", "kneighbors_graph", "radius_neighbors_graph", "sort_graph_by_row_values", "BernoulliRBM", "MLPClassifier", "MLPRegressor", "FeatureUnion", "Pipeline", "make_pipeline", "make_union", "Binarizer", "FunctionTransformer", "KBinsDiscretizer", "KernelCenterer", "LabelBinarizer", "LabelEncoder", "MaxAbsScaler", "MinMaxScaler", "MultiLabelBinarizer", "Normalizer", "OneHotEncoder", "OrdinalEncoder", "PolynomialFeatures", "PowerTransformer", "QuantileTransformer", "RobustScaler", "SplineTransformer", "StandardScaler", "TargetEncoder", "add_dummy_feature", "binarize", "label_binarize", "maxabs_scale", "minmax_scale", "normalize", "power_transform", "quantile_transform", "robust_scale", "scale", "GaussianRandomProjection", "SparseRandomProjection", "johnson_lindenstrauss_min_dim", "LabelPropagation", "LabelSpreading", "SelfTrainingClassifier", "set_config", "show_versions", "LinearSVC", "LinearSVR", "NuSVC", "NuSVR", "OneClassSVM", "SVC", "SVR", "l1_min_c", "DecisionTreeClassifier", "DecisionTreeRegressor", "ExtraTreeClassifier", "ExtraTreeRegressor", "export_graphviz", "export_text", "plot_tree", "Bunch", "ClassifierTags", "InputTags", "RegressorTags", "Tags", "TargetTags", "TransformerTags", "_safe_indexing", "min_pos", "as_float_array", "assert_all_finite", "check_X_y", "check_array", "check_consistent_length", "check_random_state", "check_scalar", "compute_class_weight", "compute_sample_weight", "deprecated", "all_displays", "all_estimators", "all_functions", "check_estimator", "estimator_checks_generator", "parametrize_with_checks", "estimator_html_repr", "density", "fast_logdet", "randomized_range_finder", "randomized_svd", "safe_sparse_dot", "weighted_mode", "gen_batches", "gen_even_slices", "get_tags", "single_source_shortest_path_length", "indexable", "MetadataRequest", "MetadataRouter", "MethodMapping", "get_routing_for_object", "process_routing", "available_if", "is_multilabel", "type_of_target", "unique_labels", "murmurhash3_32", "Parallel", "delayed", "sample_without_replacement", "resample", "safe_mask", "safe_sqr", "shuffle", "incr_mean_variance_axis", "inplace_column_scale", "inplace_csr_column_scale", "inplace_row_scale", "inplace_swap_column", "inplace_swap_row", "mean_variance_axis", "inplace_csr_row_normalize_l1", "inplace_csr_row_normalize_l2", "check_is_fitted", "check_memory", "check_symmetric", "column_or_1d", "has_fit_parameter", "validate_data", "<span class=\"section-number\">3.2. </span>Tuning the hyper-parameters of an estimator", "<span class=\"section-number\">7.4. </span>Imputation of missing values", "<span class=\"section-number\">1.15. </span>Isotonic regression", "<span class=\"section-number\">7.7. </span>Kernel Approximation", "<span class=\"section-number\">1.3. </span>Kernel ridge regression", "<span class=\"section-number\">1.2. </span>Linear and Quadratic Discriminant Analysis", "<span class=\"section-number\">3.5. </span>Validation curves: plotting scores to evaluate models", "<span class=\"section-number\">1.1. </span>Linear Models", "<span class=\"section-number\">2.2. </span>Manifold learning", "<span class=\"section-number\">7.8. </span>Pairwise metrics, Affinities and Kernels", "<span class=\"section-number\">2.1. </span>Gaussian mixture models", "<span class=\"section-number\">3.4. </span>Metrics and scoring: quantifying the quality of predictions", "<span class=\"section-number\">1.12. </span>Multiclass and multioutput algorithms", "<span class=\"section-number\">1.9. </span>Naive Bayes", "<span class=\"section-number\">1.6. </span>Nearest Neighbors", "<span class=\"section-number\">1.17. </span>Neural network models (supervised)", "<span class=\"section-number\">2.9. </span>Neural network models (unsupervised)", "<span class=\"section-number\">2.7. </span>Novelty and Outlier Detection", "<span class=\"section-number\">5.1. </span>Partial Dependence and Individual Conditional Expectation plots", "<span class=\"section-number\">5.2. </span>Permutation feature importance", "&lt;no title&gt;", "<span class=\"section-number\">7.3. </span>Preprocessing data", "<span class=\"section-number\">7.9. </span>Transforming the prediction target (<code class=\"docutils literal notranslate\"><span class=\"pre\">y</span></code>)", "<span class=\"section-number\">7.6. </span>Random Projection", "<span class=\"section-number\">1.14. </span>Semi-supervised learning", "<span class=\"section-number\">1.5. </span>Stochastic Gradient Descent", "<span class=\"section-number\">1.4. </span>Support Vector Machines", "<span class=\"section-number\">1.10. </span>Decision Trees", "<span class=\"section-number\">7.5. </span>Unsupervised dimensionality reduction", "<span class=\"section-number\">14. </span>External Resources, Videos and Talks", "Related Projects", "Roadmap", "Computation times", "<span class=\"section-number\">1. </span>Supervised learning", "Support", "Testimonials", "<span class=\"section-number\">2. </span>Unsupervised learning", "User Guide", "Available documentation for scikit-learn", "<span class=\"section-number\">6. </span>Visualizations", "Release History", "&lt;no title&gt;", "Older Versions", "Version 0.13", "Version 0.14", "Version 0.15", "Version 0.16", "Version 0.17", "Version 0.18", "Version 0.19", "Version 0.20", "Version 0.21", "Version 0.22", "Version 0.23", "Version 0.24", "Version 1.0", "Version 1.1", "Version 1.2", "Version 1.3", "Version 1.4", "Version 1.5", "Version 1.6", "Version 1.7", "Version 1.8"], "titleterms": {"": [185, 308, 310, 322, 374, 383, 410, 1000], "0": [179, 312, 313, 314, 315, 1016, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051], "1": [184, 315, 316, 317, 318, 319, 320, 321, 322, 383, 1031, 1032, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052], "10": 1031, "11": 1031, "12": 1031, "13": [321, 1032, 1050], "14": 1033, "15": 1034, "16": 1035, "17": 1036, "18": 1037, "19": 1038, "1d": [288, 352], "2": [317, 1034, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1051], "20": [102, 346, 366, 1039], "2018": 1020, "20newgroup": 220, "21": 1040, "22": [312, 1041], "23": [313, 1042], "24": [314, 1043], "2d": [52, 67, 85, 100, 122, 128, 184], "3": [64, 318, 321, 1039, 1040, 1045, 1047, 1050], "3d": 184, "4": [319, 1031, 1039, 1048], "5": [320, 1016, 1031, 1049], "6": [321, 1031, 1050], "7": [322, 1031, 1051], "8": [1031, 1052], "9": 1031, "99": 366, "A": [53, 59, 60, 81, 91, 96, 206, 315, 370, 377, 407], "And": [229, 386], "In": 270, "NOT": 376, "One": [200, 219, 272, 315, 334, 407, 1006, 1014], "The": [0, 116, 182, 183, 206, 230, 236, 257, 261, 270, 277, 280, 366, 375, 379, 407, 411, 999, 1000, 1012, 1018], "Will": 383, "With": 243, "__sklearn_is_fitted__": 132, "_safe_index": 927, "abil": 173, "ablat": 242, "about": [0, 183, 383], "absolut": 1000, "access": 404, "accuraci": [185, 237, 346, 350, 1000], "accuracy_scor": 703, "across": 354, "action": 371, "activ": [0, 325], "ad": 403, "adaboost": [134, 135, 136, 410], "adaboostclassifi": [134, 550], "adaboostregressor": 551, "adapt": 1000, "add": [319, 383], "add_dummy_featur": 887, "addit": [410, 992], "additive_chi2_kernel": 759, "additivechi2sampl": 637, "address": 257, "adjac": 56, "adjust": 73, "adjusted_mutual_info_scor": 704, "adjusted_rand_scor": 705, "advanc": 243, "advantag": 403, "affin": [52, 74, 403, 998], "affinity_propag": 450, "affinitypropag": 436, "agglom": [75, 85], "agglomer": [84, 87, 1017], "agglomerativeclust": 437, "aggress": [989, 996], "aic": [198, 996], "algebra": [358, 380], "algorithm": [58, 59, 60, 74, 79, 82, 88, 96, 97, 98, 225, 232, 377, 383, 994, 996, 1001, 1003, 1004, 1008, 1016], "alias": 379, "align": 997, "all": [263, 272, 316, 389, 1046, 1047, 1048], "all_displai": 939, "all_estim": 940, 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