dev/notebooks/auto_examples/classification/plot_classification_probability.ipynb

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        "\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"
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        "# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn import datasets\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\nplt.figure(figsize=(3 * 2, n_classifiers * 2))\nplt.subplots_adjust(bottom=0.2, top=0.95)\n\nxx = np.linspace(3, 9, 100)\nyy = np.linspace(1, 5, 100).T\nxx, yy = np.meshgrid(xx, yy)\nXfull = np.c_[xx.ravel(), yy.ravel()]\n\nfor index, (name, classifier) in enumerate(classifiers.items()):\n    classifier.fit(X, y)\n\n    y_pred = classifier.predict(X)\n    accuracy = accuracy_score(y, y_pred)\n    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n\n    # View probabilities:\n    probas = classifier.predict_proba(Xfull)\n    n_classes = np.unique(y_pred).size\n    for k in range(n_classes):\n        plt.subplot(n_classifiers, n_classes, index * n_classes + k + 1)\n        plt.title(\"Class %d\" % k)\n        if k == 0:\n            plt.ylabel(name)\n        imshow_handle = plt.imshow(\n            probas[:, k].reshape((100, 100)), extent=(3, 9, 1, 5), origin=\"lower\"\n        )\n        plt.xticks(())\n        plt.yticks(())\n        idx = y_pred == k\n        if idx.any():\n            plt.scatter(X[idx, 0], X[idx, 1], marker=\"o\", c=\"w\", edgecolor=\"k\")\n\nax = plt.axes([0.15, 0.04, 0.7, 0.05])\nplt.title(\"Probability\")\nplt.colorbar(imshow_handle, cax=ax, orientation=\"horizontal\")\n\nplt.show()"
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