[{'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:def plot_boundary(ax, estimator, title, xfeature, yfeature, X_train, X_test):\nfrom matplotlib.colors import ListedColormap\ncmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\ncmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\nx_min, x_max = X_train[xfeature].min() - 1, X_train[xfeature].max() + 1\ny_min, y_max = X_train[yfeature].min() - 1, X_train[yfeature].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, .008),\nnp.arange(y_min, y_max, .008))\nZ = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\nax.pcolormesh(xx, yy, Z, cmap=cmap_light)\nax.scatter(X_train[xfeature], X_train[yfeature], c=y_train, cmap=cmap_bold)\nax.scatter(X_test[xfeature], X_test[yfeature], c=y_test, marker='x', cmap=cmap_bold)\nax.set_xlim(xx.min(), xx.max())\nax.set_ylim(yy.min(), yy.max())\nax.set_xlabel(xfeature, fontsize=10)\nax.set_ylabel(yfeature, fontsize=10)\nax.set_title(title, fontsize=10)"}, {'role': 'assistant', 'content': 'result_visualization'}]