[{'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 noNullAntibiotics(antibiotics):\ndf = combined.loc[pd.isnull(combined[antibiotics + \'_ris\']) == 0]\nreturn df\ndef plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\nn_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n"""\nGenerate a simple plot of the test and training learning curve.\nParameters\n----------\nestimator : object type that implements the "fit" and "predict" methods\nAn object of that type which is cloned for each validation.\ntitle : string\nTitle for the chart.\nX : array-like, shape (n_samples, n_features)\nTraining vector, where n_samples is the number of samples and\nn_features is the number of features.\ny : array-like, shape (n_samples) or (n_samples, n_features), optional\nTarget relative to X for classification or regression;\nNone for unsupervised learning.\nylim : tuple, shape (ymin, ymax), optional\nDefines minimum and maximum yvalues plotted.\ncv : int, cross-validation generator or an iterable, optional\nDetermines the cross-validation splitting strategy.\nPossible inputs for cv are:\n- None, to use the default 3-fold cross-validation,\n- integer, to specify the number of folds.\n- An object to be used as a cross-validation generator.\n- An iterable yielding train/test splits.\nFor integer/None inputs, if ``y`` is binary or multiclass,\n:class:`StratifiedKFold` used. If the estimator is not a classifier\nor if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\nRefer :ref:`User Guide <cross_validation>` for the various\ncross-validators that can be used here.\nn_jobs : integer, optional\nNumber of jobs to run in parallel (default 1).\n"""\nplt.figure()\nplt.title(title)\nif ylim is not None:\nplt.ylim(*ylim)\nplt.xlabel("Training examples")\nplt.ylabel("Score")\ntrain_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\nplt.grid()\nplt.fill_between(train_sizes, train_scores_mean - train_scores_std,\ntrain_scores_mean + train_scores_std, alpha=0.1,\ncolor="r")\nplt.fill_between(train_sizes, test_scores_mean - test_scores_std,\ntest_scores_mean + test_scores_std, alpha=0.1, color="g")\nplt.plot(train_sizes, train_scores_mean, \'o-\', color="r",\nlabel="Training score")\nplt.plot(train_sizes, test_scores_mean, \'o-\', color="g",\nlabel="Cross-validation score")\nplt.legend(loc="best")\ndef trainSVMrbf(df, antibiotics):\nX_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :406], df[antibiotics + \'_ris\'], test_size=0.8, random_state=0)\nclf = svm.SVC(kernel=\'rbf\', C=1).fit(X_train, y_train)\nskf = StratifiedKFold(n_splits=3, random_state=None, shuffle=False)\ncv = skf.split(X_train, y_train)\ngammas = np.logspace(-6, -1, 10)\nclassifier = GridSearchCV(estimator=clf, cv=cv, param_grid=dict(gamma=gammas))\nclassifier.fit(X_train.values, np.asarray(y_train))\ntitle = antibiotics +\' Learning Curves (SVM, rbf kernel, $\\gamma=%.6f$)\' %classifier.best_estimator_.gamma\nestimator = svm.SVC(kernel=\'linear\', gamma=classifier.best_estimator_.gamma) #put the best parameters in\nplot_learning_curve(estimator, title, X_train.values, np.asarray(y_train), cv=skf)\nplt.show()\nprint(\'classifier score\', classifier.score(X_test, y_test))\ndef antiToSVMrbf(antibiotics):\ndf = noNullAntibiotics(antibiotics)\ntrainSVMrbf(df, antibiotics)'}, {'role': 'assistant', 'content': "['evaluation', 'modelling']"}]