[{'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 plot_decision_tree(a,b):<br>    """<br>    http://scikit-learn.org/stable/modules/tree.html<br>    """<br>    dot_data = tree.export_graphviz(a, out_file=None, feature_names=b,class_names=[\'Healthy\',\'Diabetes\'],filled=False, rounded=True,special_characters=False)  <br>    graph = graphviz.Source(dot_data)  <br>    return graph <br><br>def plot_confusion_matrix(cm, classes,normalize=False,title=\'Confusion matrix\',cmap=plt.cm.Blues):<br>    """<br>    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html<br>    """<br>    plt.imshow(cm, interpolation=\'nearest\', cmap=cmap)<br>    plt.title(title)<br>    plt.colorbar()<br>    tick_marks = np.arange(len(classes))<br>    plt.xticks(tick_marks, classes, rotation=45)<br>    plt.yticks(tick_marks, classes)<br>    fmt = \'.2f\' if normalize else \'d\'<br>    thresh = cm.max() / 2.<br>    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):<br>        plt.text(j, i, format(cm[i, j], fmt),horizontalalignment="center",color="white" if cm[i, j] > thresh else "black")<br>    plt.tight_layout()<br>    plt.ylabel(\'True label\')<br>    plt.xlabel(\'Predicted label\')<br>    <br>models = []<br>models.append(("LR",LogisticRegression()))<br>models.append(("NB",GaussianNB()))<br>models.append(("KNN",KNeighborsClassifier()))<br>models.append(("DT",DecisionTreeClassifier()))<br>models.append(("SVM",SVC()))'}, {'role': 'assistant', 'content': "['result_visualization']"}]