[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:# testing various classifiers to choose the best accuracy.<br><br># linear SVM<br># clf = svm.SVC()<br># clf.fit(X, y)<br>#<br># SVM with sigmoid kernel<br># clf = svm.SVC(kernel=\'sigmoid\')<br># clf.fit(X, y)<br>#<br># SVM with rbf kernel<br># clf = svm.SVC(kernel=\'rbf\')<br># clf.fit(X, y)<br>#<br># SVM with poly kernel<br># clf = svm.SVC(kernel=\'poly\')<br># clf.fit(X, y)<br>#<br># adaboost <br># clf = AdaBoostClassifier(n_estimators = 350)<br># clf.fit(X, y)<br># <br># random forest<br># clf = RandomForestClassifier(n_estimators = 250)<br># clf.fit(X, y)<br># <br># decision tree<br># clf = DecisionTreeClassifier()<br># clf.fit(X, y)<br>#<br># extra tree<br># clf = ExtraTreesClassifier()<br># clf.fit(X, y)<br>#<br># gaussian naive bayes<br># clf = GaussianNB()<br># clf.fit(X, y)<br>#<br># logistic regression<br># clf = linear_model.LogisticRegression()<br># clf.fit(X, y)<br># <br># stochastic gradient descent<br># clf = SGDClassifier(loss="squared_loss", penalty="l2")<br># clf = SGDClassifier(loss="hinge", penalty="l2")<br># clf.fit(X, y)<br># <br># multi layer perceptron<br># scaler = StandardScaler()<br># scaler.fit(X)<br># X = scaler.transform(X)<br># test_X = scaler.transform(test_X)<br># print (scaler)<br># print (X)<br># print (test_X)<br># clf = MLPClassifier(solver=\'lbfgs\', alpha=1e-4, hidden_layer_sizes=(10,5), warm_start=\'True\')<br># clf.fit(X, y)<br># <br># Gradient boosting<br>params = {\'n_estimators\': 2000, \'learning_rate\': 0.008}<br>clf = ensemble.GradientBoostingClassifier(**params)<br>clf.fit(X, y)<br><br>cols = { \'PlayerID\': [i+901 for i in range(440)] , \'TARGET_5Yrs\': [clf.predict([test_X[i]])[0] for i in range(440)] }<br>submission = pd.DataFrame(cols)<br>print(submission)<br>submission.to_csv("submission.csv", index=False)'}, {'role': 'assistant', 'content': 'modelling'}]