[{'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:<br>#model = DecisionTreeClassifier(max_depth=8,min_samples_leaf=2)<br>#0.70-no norm . 0.76--w/ normalization<br>"""<br>model = LogisticRegression(penalty=\'l2\', dual=False, tol=0.0001, <br>                           C=1.0, fit_intercept=True, intercept_scaling=1, <br>                           class_weight=None, random_state=10, solver=\'liblinear\', <br>                           max_iter=100, multi_class=\'ovr\', verbose=0, warm_start=False, n_jobs=1)  <br>"""<br>#model =GaussianNB()   #74.17<br>#model= RandomForestClassifier(max_depth=6, random_state=0)<br>#""" %77.00<br>model= RandomForestClassifier(bootstrap=True, class_weight=None, criterion=\'gini\',<br>            max_features=\'auto\', max_leaf_nodes=None,<br>            min_impurity_decrease=0.0, min_impurity_split=None,<br>            min_samples_leaf=1, min_samples_split=2,<br>            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,<br>            random_state=10, verbose=0, warm_start=False)<br>#"""<br>#model = AdaBoostClassifier(DecisionTreeClassifier(max_depth=6), n_estimators=150, random_state=10) #74<br><br>"""70.07<br>model = MLPClassifier(activation=\'relu\', alpha=1e-05, batch_size=\'auto\',<br>       beta_1=0.9, beta_2=0.999, early_stopping=False,<br>       epsilon=1e-08, hidden_layer_sizes=(5, 2), learning_rate=\'constant\',<br>       learning_rate_init=0.001, max_iter=200, momentum=0.9,<br>       nesterovs_momentum=True, power_t=0.5, random_state=10, shuffle=True,<br>       solver=\'lbfgs\', tol=0.0001, validation_fraction=0.1, verbose=False,<br>       warm_start=False)<br>"""<br>#model = LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=\'auto\', solver=\'eigen\', store_covariance=False, tol=0.0001)<br>#model = QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,  store_covariance=False, store_covariances=None, tol=0.0001)<br><br>#SVC: rbf(64) linear(76) poly-3<br>"""<br>model = SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,<br>    decision_function_shape=\'ovr\', degree=2, gamma=\'auto\', kernel=\'linear\',<br>    max_iter=-1, probability=False, random_state=None, shrinking=True,<br>    tol=0.001, verbose=False)<br>"""<br>#gradientBoostingClassifier . 0.77<br>#params = {\'max_depth\':9, \'subsample\':0.5, \'learning_rate\':0.01, \'min_samples_leaf\':1, \'random_state\':0}<br>#model = GradientBoostingClassifier(n_estimators=290,**params)<br><br>#model = GradientBoostingClassifier()<br><br>X_train, X_test, y_train, y_test = train_test_split(trainingFeatures2, trainingLabels, test_size=0.1, random_state=10)<br><br><br>#original<br>model.fit(X_train, y_train)<br><br>#scaler = StandardScaler()<br>#X_train_scaler = scaler.fit_transform(X_train)<br>#model.fit(X_train_scaler, y_train)<br><br>columns = trainingFeatures2.columns<br>feature_names = trainingFeatures2.columns.values<br><br>#coefficients = model.feature_importances_.reshape(trainingFeatures2.columns.shape[0], 1)<br>#absCoefficients = abs(coefficients)<br>#fullList = pd.concat((pd.DataFrame(columns, columns = [\'Feature\']), pd.DataFrame(absCoefficients, columns = [\'absCoefficient\'])), axis = 1).sort_values(by=\'absCoefficient\', ascending = False)<br>#print(\'\\<br>Feature Importance:\\<br>\\<br>\',fullList,\'\\<br>\')<br>#plot_decision_tree(model,feature_names)'}, {'role': 'assistant', 'content': 'data_preprocessing'}]