[{'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 svm_train(X, y):\nN = X.shape[0]\nP = cvxopt.matrix(svm_kernel(X, X) * np.outer(y, y))\nq = cvxopt.matrix(-np.ones(N))\nG = cvxopt.matrix(-1.0 * np.eye(N))\nh = cvxopt.matrix(0.0, (N, 1))\nA = cvxopt.matrix(y, (1, N))\nb = cvxopt.matrix(0.0)\nsolution = cvxopt.solvers.qp(P, q, G, h, A, b)\nalpha = np.array(solution["x"]).reshape(-1)\nbeta = np.sum(alpha * y * svm_kernel(X, X).T, axis = 1)\nbeta0 = -(np.max(beta[y > 0]) + np.min(beta[y < 0])) / 2.\nreturn (alpha, beta0)\n(alpha, beta0) = svm_train(X_train, y_train)\nprint("beta0 =", beta0)\nplt.hist(alpha);'}, {'role': 'assistant', 'content': "['modelling', 'result_visualization']"}]