[{'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:import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.datasets import make_moons, make_circles, make_classification\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import LogisticRegression\nh = .02  # step size in the mesh\nnames = ["Nearest Neighbors", \'Logistic Regression\', "Linear SVM", "RBF SVM", "Decision Tree", "Gaussian NB"]\nclassifiers = [\nKNeighborsClassifier(3),\nLogisticRegression(),\nSVC(kernel="linear", C=0.025),\nSVC(gamma=2, C=1),\nDecisionTreeClassifier(max_depth=5),\nGaussianNB(),\n]\nX, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\nrandom_state=1, n_clusters_per_class=1)\nrng = np.random.RandomState(2)\nX += 2 * rng.uniform(size=X.shape)\nlinearly_separable = (X, y)\ndatasets = [make_moons(noise=0.3, random_state=0),\nmake_circles(noise=0.2, factor=0.5, random_state=1),\nlinearly_separable\n]\nfigure = plt.figure(figsize=(27, 9))\ni = 1\nfor ds_cnt, ds in enumerate(datasets):\nX, y = ds\nX = StandardScaler().fit_transform(X)\nX_train, X_test, y_train, y_test = \\\ntrain_test_split(X, y, test_size=.4, random_state=42)\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h),\nnp.arange(y_min, y_max, h))\ncm = plt.cm.RdBu\ncm_bright = ListedColormap([\'#FF0000\', \'#0000FF\'])\nax = plt.subplot(len(datasets), len(classifiers) + 1, i)\nif ds_cnt == 0:\nax.set_title("Input data")\nax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\nax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\nax.set_xlim(xx.min(), xx.max())\nax.set_ylim(yy.min(), yy.max())\nax.set_xticks(())\nax.set_yticks(())\ni += 1\nfor name, clf in zip(names, classifiers):\nax = plt.subplot(len(datasets), len(classifiers) + 1, i)\nclf.fit(X_train, y_train)\nscore = clf.score(X_test, y_test)\nif hasattr(clf, "decision_function"):\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nelse:\nZ = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\nZ = Z.reshape(xx.shape)\nax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\nax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\nax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\nalpha=0.6)\nax.set_xlim(xx.min(), xx.max())\nax.set_ylim(yy.min(), yy.max())\nax.set_xticks(())\nax.set_yticks(())\nif ds_cnt == 0:\nax.set_title(name)\nax.text(xx.max() - .3, yy.min() + .3, (\'%.2f\' % score).lstrip(\'0\'),\nsize=15, horizontalalignment=\'right\')\ni += 1\nplt.tight_layout()\nplt.show()'}, {'role': 'assistant', 'content': "['helper_functions', 'evaluation', 'modelling', 'prediction']"}]