将下列代码转换为matlab格式： from sklearn.model_selection import train_test_split train_x, test_x, train_y, test_y = train_test_split(np.array(data.iloc[:, [3,4,5,6,7,9,10,11,12,13,14,15]]), np.array(data.iloc[:, [5]]), test_size = 0.3) import xgboost as xgb from xgboost import plot_importance model = xgb.XGBRegressor(silent = 0, learning_rate = 0.05, eta = 0.1, gamma = 0.25, max_depth = 24, reg_lambda = 2, reg_alpha = 1, n_estimators = 300, seed = 999, objective ='reg:squarederror') model.fit(train_x, train_y, eval_metric = "rmse", eval_set = [(train_x, train_y), (test_x, test_y)], verbose = True)

以下是将给定的 Python 代码转换为 MATLAB 代码的示例：

% 加载数据
data = readtable('data.csv');
train_x = table2array(data(:, [4,5,6,7,8,10,11,12,13,14,15,16]));
train_y = table2array(data(:, [6]));

% 拆分数据集
rng(42); % 设定随机数种子
[test_x, train_x, test_y, train_y] = split_data(train_x, train_y, 0.3);

% 训练 XGBoost 模型
model = xgboost;
model.silent = 0;
model.learning_rate = 0.05;
model.eta = 0.1;
model.gamma = 0.25;
model.max_depth = 24;
model.reg_lambda = 2;
model.reg_alpha = 1;
model.n_estimators = 300;
model.seed = 999;
model.objective = 'reg:squarederror';

% 训练模型并进行评估
model = fit(model, train_x, train_y, 'eval_set', [train_x, train_y; test_x, test_y], 'eval_metric', 'rmse', 'verbose', true);

在这个示例中，我们首先使用 `readtable` 函数加载数据。然后，我们将输入和输出变量分别存储在 `train_x` 和 `train_y` 中。接下来，我们使用 `split_data` 函数将数据集拆分为训练集和测试集。最后，我们定义了 XGBoost 模型的各种超参数，并使用 `fit` 函数进行训练和评估。