[{'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:def analysis(train_df_x, train_df_y, test_df_x, test_df_y):\nclf = DecisionTreeClassifier(criterion=\'entropy\')\nclf = clf.fit(train_df_x, train_df_y)\nfeature_importances = clf.feature_importances_\nplt.figure()\nplt.title(\'Feature Importances bar plot\')\nplt.bar(range(train_df_x.shape[1]), feature_importances, align=\'center\')\nplt.xticks(range(train_df_x.shape[1]), train_df_x.columns, rotation=\'vertical\')\nplt.xlabel("Features")\nplt.ylabel("Importance")\nplt.show()\nplt.close("all")\norder = np.argsort(feature_importances)[::-1]\nprint("Top 5 most important features")\nfor n,i in enumerate(order[:5]):\nprint("%d"%(n+1),train_df_x.columns[i],feature_importances[i])\ncm = confusion_matrix(clf.predict(train_df_x), train_df_y)\nprint("\\nAccuracy on train_df: ",(cm[0][0] + cm[1][1]) / float(sum(sum(cm))))\ncm2 = confusion_matrix(clf.predict(test_df_x), test_df_y)\nprint("Accuracy on test_df: ",(cm2[0][0] + cm2[1][1]) / float(sum(sum(cm2))))\nmin_samples_split_values = [round(int(len(train_df_x)*0.01*i),-1) for i in range(1,11,1)]#np.arange(300,3001,300)\nmin_samples_leaf_values = [round(int(len(train_df_x)*0.05*i*0.1),-1) + 1 for i in range(1,11,1)]\nresult = dict()\nfor i in min_samples_split_values:\nresult[i] = list()\nfor j in min_samples_leaf_values:\nclf = DecisionTreeClassifier(criterion=\'entropy\', min_samples_leaf = j, min_samples_split= i)\nclf = clf.fit(train_df_x, train_df_y)\ncm = confusion_matrix(clf.predict(test_df_x), test_df_y)\nresult[i].append((cm[0][0] + cm[1][1]) / float(sum(sum(cm))))\nplt.figure()\ncolors = [\'b\',\'r\',\'y\',\'g\',\'k\',\'orange\',\'c\',\'m\',\'pink\',\'gray\',\'firebrick\']\nfor n,i in enumerate(min_samples_split_values):\nplt.plot(min_samples_leaf_values, result[i], color = colors[n], label = \'Split={}\'.format(i))\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.xlabel(\'Min Leaf Size\')\nplt.ylabel(\'Accuracy from Confusion matrix\')\nplt.title("Decision Tree with different min_leaf, min_split parameters")\nplt.show()\nplt.close("all")\nlogreg = linear_model.LogisticRegression(C = 1e30)\nlogreg.fit(train_df_x, train_df_y)\npred_lr = logreg.predict_proba(test_df_x)[:,1]\nfpr_lr, tpr_lr, thre_lr = metrics.roc_curve(test_df_y, pred_lr)\nroc_auc_lr = metrics.auc(fpr_lr, tpr_lr)\nmy_svm = svm.SVC(kernel=\'linear\')\nmy_svm.fit(train_df_x, train_df_y)\npred_svm = my_svm.decision_function(test_df_x)\nfpr_svm, tpr_svm, thre_svm = metrics.roc_curve(test_df_y, pred_svm)\nroc_auc_svm = metrics.auc(fpr_svm, tpr_svm)\nfig = plt.figure(figsize = (6, 5))\nplt.title(\'ROC Curve\')\nplt.xlabel(\'fpr\')\nplt.ylabel(\'tpr\')\nplt.xlim([0,1])\nplt.ylim([0,1])\nplt.plot([0,1],[0,1],\'r--\', label=\'\')\nplt.plot(fpr_lr, tpr_lr, label=\'AUC log_reg({0:.3f})\'.format(roc_auc_lr))\nplt.plot(fpr_svm, tpr_svm, label=\'AUC SVM({0:.3f})\'.format(roc_auc_svm))\nplt.legend(loc=4)\nreturn pred_lr'}, {'role': 'assistant', 'content': "['evaluation', 'modelling', 'result_visualization']"}]