[{'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:start = time()\ndataset_training = dataset_training.sample(frac=0.6, replace=True)\nX = dataset_training.drop([\'Outcome\',\'Class\'],axis=1).values\ny = dataset_training[\'Class\'].values\ntarget_names = [\'non-mineralized\',\'mineralized\']\nfrom itertools import cycle\nfrom sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\nfrom sklearn import ensemble\nfrom sklearn import metrics\nfrom scipy import interp\ncv = StratifiedShuffleSplit(n_splits=5,test_size=0.33)\nclf = ensemble.RandomForestClassifier(n_estimators=1000, random_state=123)\nmean_tpr = 0.0\nmean_fpr = np.linspace(0, 1, 100)\ncolors = cycle([\'cyan\', \'indigo\', \'seagreen\', \'yellow\', \'blue\', \'darkorange\'])\nlw = 2\nfrom collections import Counter\nprediciton = []\ni = 0\nfor (train, test), color in zip(cv.split(X, y), colors):\ncnt = Counter(y[train])\nw0 = cnt[1]/len(y[train]) # weight associated to 0\'s\nw1 = cnt[0]/len(y[train]) # weight associated to 1\'s\nclf.fit(X[train], y[train], sample_weight=np.array([w0 if r==0 else w1 for r in y[train]]))\nprobas_ = clf.predict_proba(X[test])\npreds_ = clf.predict(X[test])\nfpr, tpr, thresholds = metrics.roc_curve(y[test], probas_[:, 1])\nmean_tpr += interp(mean_fpr, fpr, tpr)\nmean_tpr[0] = 0.0\nroc_auc = metrics.auc(fpr, tpr)\nACC = metrics.accuracy_score(y[test],preds_)\nprecision, recall, thresholds = metrics.precision_recall_curve(y[test], probas_[:, 1])\ncm = metrics.confusion_matrix(y[test],preds_)\nprint(\'=\'*30)\nprint(\'******** Result fold {} *********\'.format(i+1))\nprint("TP:{}".format(cm[1,1], cm[1,1]/(cm.sum()+0.0)))\nprint("FP:{}".format(cm[0,1], cm[0,1]/(cm.sum()+0.0)))\nprint(\'Accuracy: {:.4%}\'.format(ACC))\nprint(\'Precision: {:.4%}\'.format(metrics.precision_score(y[test], clf.predict(X[test]))))\nprint(\'Recall: {:.4%}\'.format(metrics.recall_score(y[test], clf.predict(X[test]))))\nprint(\'F1-score: {:.4%}\'.format(metrics.f1_score(y[test], clf.predict(X[test]))))\nprint(\'\\n******** Confusion matrix fold {} *********\'.format(i+1))\nprint(pd.crosstab(y[test], preds_, rownames=[\'True\'], colnames=[\'Predicted\'], margins=True))\nplt.plot(fpr, tpr, lw=lw, color=color,\nlabel=\'ROC fold %d (area = %0.2f)\' % (i, roc_auc))\ni += 1\nplt.plot([0, 1], [0, 1], linestyle=\'--\', lw=lw, color=\'k\',\nlabel=\'Luck\')\nmean_tpr /= cv.get_n_splits(X, y)\nmean_tpr[-1] = 1.0\nmean_auc = metrics.auc(mean_fpr, mean_tpr)\nplt.plot(mean_fpr, mean_tpr, color=\'g\', linestyle=\'--\',\nlabel=\'Mean ROC (area = %0.2f)\' % mean_auc, lw=lw)\nplt.xlim([-0.05, 1.05])\nplt.ylim([-0.05, 1.05])\nplt.xlabel(\'False Positive Rate\')\nplt.ylabel(\'True Positive Rate\')\nplt.title(\'Receiver operating characteristic\')\nplt.legend(loc="lower right")\nplt.show()\nprint(\'Total running time of the script {:.2f} seconds\'.format(time() - start))'}, {'role': 'assistant', 'content': "['helper_functions', 'evaluation', 'modelling', 'prediction']"}]