Pushing the docs to 1.5/ for branch: 1.5.X, commit a5cf4cea0f753dcf649520f68c0c0f27d73dc1d4

Author: sklearn-ci

Diff for: 1.5/.buildinfo

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Diff for: 1.5/_downloads/07960f9087d379e9d0da6350d6ee3f41/plot_classification_probability.ipynb

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       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot classification probability\n\nPlot the classification probability for different classifiers. We use a 3 class\ndataset, and we classify it with a Support Vector classifier, L1 and L2\npenalized logistic regression with either a One-Vs-Rest or multinomial setting,\nand Gaussian process classification.\n\nLinear SVC is not a probabilistic classifier by default but it has a built-in\ncalibration option enabled in this example (`probability=True`).\n\nThe logistic regression with One-Vs-Rest is not a multiclass classifier out of\nthe box. As a result it has more trouble in separating class 2 and 3 than the\nother estimators.\n"
+        "\n# Plot classification probability\n\nPlot the classification probability for different classifiers. We use a 3 class\ndataset, and we classify it with a Support Vector classifier, L1 and L2\npenalized logistic regression (multinomial multiclass), a One-Vs-Rest version with\nlogistic regression, and Gaussian process classification.\n\nLinear SVC is not a probabilistic classifier by default but it has a built-in\ncalibration option enabled in this example (`probability=True`).\n\nThe logistic regression with One-Vs-Rest is not a multiclass classifier out of\nthe box. As a result it has more trouble in separating class 2 and 3 than the\nother estimators.\n"
       ]
     },
     {
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Alexandre Gramfort <[email protected]>\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import cm\n\nfrom sklearn import datasets\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.svm import SVC\n\niris = datasets.load_iris()\nX = iris.data[:, 0:2]  # we only take the first two features for visualization\ny = iris.target\n\nn_features = X.shape[1]\n\nC = 10\nkernel = 1.0 * RBF([1.0, 1.0])  # for GPC\n\n# Create different classifiers.\nclassifiers = {\n    \"L1 logistic\": LogisticRegression(\n        C=C, penalty=\"l1\", solver=\"saga\", multi_class=\"multinomial\", max_iter=10000\n    ),\n    \"L2 logistic (Multinomial)\": LogisticRegression(\n        C=C, penalty=\"l2\", solver=\"saga\", multi_class=\"multinomial\", max_iter=10000\n    ),\n    \"L2 logistic (OvR)\": LogisticRegression(\n        C=C, penalty=\"l2\", solver=\"saga\", multi_class=\"ovr\", max_iter=10000\n    ),\n    \"Linear SVC\": SVC(kernel=\"linear\", C=C, probability=True, random_state=0),\n    \"GPC\": GaussianProcessClassifier(kernel),\n}\n\nn_classifiers = len(classifiers)\n\nfig, axes = plt.subplots(\n    nrows=n_classifiers,\n    ncols=len(iris.target_names),\n    figsize=(3 * 2, n_classifiers * 2),\n)\nfor classifier_idx, (name, classifier) in enumerate(classifiers.items()):\n    y_pred = classifier.fit(X, y).predict(X)\n    accuracy = accuracy_score(y, y_pred)\n    print(f\"Accuracy (train) for {name}: {accuracy:0.1%}\")\n    for label in np.unique(y):\n        # plot the probability estimate provided by the classifier\n        disp = DecisionBoundaryDisplay.from_estimator(\n            classifier,\n            X,\n            response_method=\"predict_proba\",\n            class_of_interest=label,\n            ax=axes[classifier_idx, label],\n            vmin=0,\n            vmax=1,\n        )\n        axes[classifier_idx, label].set_title(f\"Class {label}\")\n        # plot data predicted to belong to given class\n        mask_y_pred = y_pred == label\n        axes[classifier_idx, label].scatter(\n            X[mask_y_pred, 0], X[mask_y_pred, 1], marker=\"o\", c=\"w\", edgecolor=\"k\"\n        )\n        axes[classifier_idx, label].set(xticks=(), yticks=())\n    axes[classifier_idx, 0].set_ylabel(name)\n\nax = plt.axes([0.15, 0.04, 0.7, 0.02])\nplt.title(\"Probability\")\n_ = plt.colorbar(\n    cm.ScalarMappable(norm=None, cmap=\"viridis\"), cax=ax, orientation=\"horizontal\"\n)\n\nplt.show()"
+        "# Author: Alexandre Gramfort <[email protected]>\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import cm\n\nfrom sklearn import datasets\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import SVC\n\niris = datasets.load_iris()\nX = iris.data[:, 0:2]  # we only take the first two features for visualization\ny = iris.target\n\nn_features = X.shape[1]\n\nC = 10\nkernel = 1.0 * RBF([1.0, 1.0])  # for GPC\n\n# Create different classifiers.\nclassifiers = {\n    \"L1 logistic\": LogisticRegression(C=C, penalty=\"l1\", solver=\"saga\", max_iter=10000),\n    \"L2 logistic (Multinomial)\": LogisticRegression(\n        C=C, penalty=\"l2\", solver=\"saga\", max_iter=10000\n    ),\n    \"L2 logistic (OvR)\": OneVsRestClassifier(\n        LogisticRegression(C=C, penalty=\"l2\", solver=\"saga\", max_iter=10000)\n    ),\n    \"Linear SVC\": SVC(kernel=\"linear\", C=C, probability=True, random_state=0),\n    \"GPC\": GaussianProcessClassifier(kernel),\n}\n\nn_classifiers = len(classifiers)\n\nfig, axes = plt.subplots(\n    nrows=n_classifiers,\n    ncols=len(iris.target_names),\n    figsize=(3 * 2, n_classifiers * 2),\n)\nfor classifier_idx, (name, classifier) in enumerate(classifiers.items()):\n    y_pred = classifier.fit(X, y).predict(X)\n    accuracy = accuracy_score(y, y_pred)\n    print(f\"Accuracy (train) for {name}: {accuracy:0.1%}\")\n    for label in np.unique(y):\n        # plot the probability estimate provided by the classifier\n        disp = DecisionBoundaryDisplay.from_estimator(\n            classifier,\n            X,\n            response_method=\"predict_proba\",\n            class_of_interest=label,\n            ax=axes[classifier_idx, label],\n            vmin=0,\n            vmax=1,\n        )\n        axes[classifier_idx, label].set_title(f\"Class {label}\")\n        # plot data predicted to belong to given class\n        mask_y_pred = y_pred == label\n        axes[classifier_idx, label].scatter(\n            X[mask_y_pred, 0], X[mask_y_pred, 1], marker=\"o\", c=\"w\", edgecolor=\"k\"\n        )\n        axes[classifier_idx, label].set(xticks=(), yticks=())\n    axes[classifier_idx, 0].set_ylabel(name)\n\nax = plt.axes([0.15, 0.04, 0.7, 0.02])\nplt.title(\"Probability\")\n_ = plt.colorbar(\n    cm.ScalarMappable(norm=None, cmap=\"viridis\"), cax=ax, orientation=\"horizontal\"\n)\n\nplt.show()"
       ]
     }
   ],