[{'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_2d_classification(classifier, X, fill=False, ax=None, eps=None, alpha=1):\nif eps is None:\neps = X.std() / 2.\nif ax is None:\nax = plt.gca()\nx_min, x_max = X[:, 0].min() - eps, X[:, 0].max() + eps\ny_min, y_max = X[:, 1].min() - eps, X[:, 1].max() + eps\nxx = np.linspace(x_min, x_max, 1000)\nyy = np.linspace(y_min, y_max, 1000)\nX1, X2 = np.meshgrid(xx, yy)\nX_grid = np.c_[X1.ravel(), X2.ravel()]\ndecision_values = classifier.predict(X_grid)\nax.imshow(decision_values.reshape(X1.shape), extent=(x_min, x_max,\ny_min, y_max),\naspect='auto', origin='lower', alpha=alpha)\nax.set_xlim(x_min, x_max)\nax.set_ylim(y_min, y_max)\nax.set_xticks(())\nax.set_yticks(())"}, {'role': 'assistant', 'content': "['modelling', 'prediction', 'result_visualization']"}]