[{'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:class k_fold_CV():\n"""\nClass for SVM algorithm which makes it easy to compute k-fold cross-validation on it.\n"""\ndef __init__(self, C, n_splits, n_strats = 10):\n\'\'\'\n- "C"         SVM hyperparameter\n- "n_splits"  number of folds of the k-fold cross validation (at least 2)\n- "n_strats"  depth of the stratification considered for the stratified k-fold. The variable is the number of\ncell lines against which the molecule is active.\n\'\'\'\nself.C = C\nself.n_splits = n_splits\nself.n_strats = n_strats\nself.trained = False\ndef fit_predict(self, X, y, verbose=True):\nself.n, self.m = y.shape\n\' we build "n_splits" folds \'\nskf = StratifiedKFold(n_splits=self.n_splits)\n\' create temporary variable for the stratification purpose \'\nlistOfPercent = np.percentile(np.sum(y, axis = 1),list((np.arange(self.n_strats))*10))\nbins = np.digitize(np.sum(y, axis = 1), listOfPercent)\nmodel = SVC(C=self.C, kernel=\'precomputed\')\nif verbose:\nprint("Fold \\t Computation time")\nk = 0\nfor train, test in skf.split(X, bins):\n\' we perform k-fold cross validation \'\nstartTime = time.time()\nX_train, y_train = X[train[:, None],train], y[train]\nX_test, y_test = X[test[:, None], train], y[test]\nY_preds_j = []\nfor j in range(y.shape[1]):\n\' we train j distinct models for each cancer cell line \'\nmodel.fit(X_train, y_train[:,j])\n\' we stack the results iteratively \'\nY_preds_j.append(model.predict(X_test))\n\' We stack the results obtained for each fold \'\nif k==0:\nself.Y_preds = np.array(Y_preds_j).T\nelse:\nself.Y_preds = np.concatenate((self.Y_preds, np.array(Y_preds_j).T))\nrunTime = time.time() - startTime\nif verbose:\nprint("%d/%d \\t %d" %(k+1, self.n_splits, runTime))\nk += 1\n\' we calculate the classification error \'\nself.accuracies = np.array([accuracy_score(self.Y_preds[:,i], y[:,i]) for i in range(self.Y_preds.shape[1])])\nself.f1_scores = np.array([f1_score(y[:,i], self.Y_preds[:,i]) for i in range(self.Y_preds.shape[1])])\nself.trained = True\ndef results(self):\nif self.trained:\nprint("Results for %d folds on the full dataset:" %self.n_splits)\nprint("Average accuracy = %.2f%%" %(np.mean(self.accuracies)*100))\nprint("Standard deviation (accuracy) = %.2f%%" %(np.std(self.accuracies)*100))\nprint("-----------------------------------------------------------")\nprint("Average F1 score = %.2f%%" % (np.mean(self.f1_scores)*100))\nprint("Standard deviation (F1 score) = %.2f%%" %(np.std(self.f1_scores)*100))\nelse:\nprint("Not trained yet.")'}, {'role': 'assistant', 'content': "['evaluation', 'modelling', 'prediction']"}]