[{'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:from sklearn.metrics import accuracy_score, log_loss<br>from sklearn.neighbors import KNeighborsClassifier<br>from sklearn.tree import DecisionTreeClassifier<br>from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier<br>from sklearn.naive_bayes import GaussianNB<br>from sklearn.discriminant_analysis import LinearDiscriminantAnalysis<br>from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis<br><br>classifiers = [<br>    KNeighborsClassifier(3),<br>    DecisionTreeClassifier(),<br>    RandomForestClassifier(),<br>    AdaBoostClassifier(),<br>    GradientBoostingClassifier(),<br>    GaussianNB(),<br>    LinearDiscriminantAnalysis(),<br>    QuadraticDiscriminantAnalysis()]<br><br># Logging for Visual Comparison<br>log_cols=["Classifier", "Accuracy", "Log Loss"]<br>log = pd.DataFrame(columns=log_cols)<br><br>for clf in classifiers:<br>    clf.fit(X_train, y_train)<br>    name = clf.__class__.__name__<br>    <br>    print("="*30)<br>    print(name)<br>    <br>    print(\'****Results****\')<br>    train_predictions = clf.predict(X_test)<br>    acc = accuracy_score(y_test, train_predictions)<br>    print("Accuracy: {:.4%}".format(acc))<br>    <br>    train_predictions = clf.predict_proba(X_test)<br>    ll = log_loss(y_test, train_predictions)<br>    print("Log Loss: {}".format(ll))<br>    <br>    log_entry = pd.DataFrame([[name, acc*100, ll]], columns=log_cols)<br>    log = log.append(log_entry)<br>    <br>print("="*30)'}, {'role': 'assistant', 'content': "['modelling']"}]