[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:print(__doc__)\nimport itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm, neighbors, tree, datasets\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn import metrics\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import cross_val_predict\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\ndef plot_classification(i, classificator):\nZ = clf.predict(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\nplt.contourf(xx, yy, Z, cmap=cmap_light, alpha=0.8)\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\nplt.xlabel(\'Sepal length\')\nplt.ylabel(\'Sepal width\')\nplt.xlim(xx.min(), xx.max())\nplt.ylim(yy.min(), yy.max())\nplt.xticks(())\nplt.yticks(())\nplt.title(titles[i])\ndef plot_confusion_matrix(cm, classes,\nnormalize=False,\ntitle=\'Confusion matrix\',\ncmap=plt.cm.Blues):\n"""\nThis function prints and plots the confusion matrix.\nNormalization can be applied by setting `normalize=True`.\n"""\nplt.imshow(cm, interpolation=\'nearest\', cmap=cmap)\nplt.title(title)\nplt.colorbar()\ntick_marks = np.arange(len(classes))\nplt.xticks(tick_marks, classes, rotation=45)\nplt.yticks(tick_marks, classes)\nif normalize:\ncm = cm.astype(\'float\') / cm.sum(axis=1)[:, np.newaxis]\nprint("Normalized confusion matrix")\nelse:\nprint(\'Confusion matrix, without normalization\')\nprint(cm)\nthresh = cm.max() / 2.\nfor i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\nplt.text(j, i, cm[i, j],\nhorizontalalignment="center",\ncolor="white" if cm[i, j] > thresh else "black")\nplt.tight_layout()\nplt.ylabel(\'True label\')\nplt.xlabel(\'Predicted label\')\ntitles = [\'SVC with linear kernel\',\n\'SVC with RBF kernel\',\n\'SVC with polynomial (degree 3) kernel\',\n\'Nearest Neighbors (Uniform)\',\n\'Nearest Neighbors (Distance)\',\n\'Decision Tree Classifier\']\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features. We could\ny = iris.target\nclass_names = iris.target_names\npylab.rcParams[\'figure.figsize\'] = (7, 7)\nh = .02  # step size in the mesh\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h),\nnp.arange(y_min, y_max, h))\ncmap_light = ListedColormap([\'#FFAAAA\', \'#AAFFAA\', \'#AAAAFF\'])\ncmap_bold = ListedColormap([\'#FF0000\', \'#00FF00\', \'#0000FF\'])\nX_train, X_test, y_train, y_test = train_test_split(\nX, y, test_size=0.4, random_state=0)\nX_train.shape, y_train.shape\nX_test.shape, y_test.shape\nC = 1.0  # SVM regularization parameter\nsvc = svm.SVC(kernel=\'linear\', C=C)\nrbf_svc = svm.SVC(kernel=\'rbf\', gamma=0.7, C=C)\npoly_svc = svm.SVC(kernel=\'poly\', degree=3, C=C)\nn_neighbors = 15\nnn_uni = neighbors.KNeighborsClassifier(n_neighbors, \'uniform\')\nnn_dist = neighbors.KNeighborsClassifier(n_neighbors, \'distance\')\ntree = tree.DecisionTreeClassifier()\nfor i, clf in enumerate((svc, rbf_svc, poly_svc, nn_uni, nn_dist, tree)):\nprint(\'######## \' + titles[i] + \' #########\')\nscores = cross_val_score(clf, X, y, cv=5)\nprint("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2) + \'\\n\')\nclf = clf.fit(X_train, y_train)\ny_pred = clf.predict(X_test)\nprint(classification_report(y_test, y_pred , target_names=class_names) + "\\n")\nplot_classification(i,clf)\nplt.show()\ncnf_matrix = confusion_matrix(y_test, y_pred)\nnp.set_printoptions(precision=2)\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names,\ntitle=\'Confusion matrix, without normalization\')\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\ntitle=\'Normalized confusion matrix\')\nplt.show()'}, {'role': 'assistant', 'content': "['load_data', 'data_exploration', 'evaluation', 'modelling']"}]