我想在基于 sklearn 庫的 Titanic ML 解決方案中實(shí)現(xiàn)梯度提升分類器。我在 Ubuntu 18.04 上使用 VS Code。我試過了：# Splitting the Training Datafrom sklearn.model_selection import train_test_splitpredictors = train.drop(['Survived', 'PassengerId'], axis=1)target = train["Survived"]x_train, x_val, y_train, y_val = train_test_split(predictors, target, test_size = 0.22, random_state = 0)# Gradient Boosting Classifierfrom sklearn.ensemble import GradientBoostingClassifiergbk = GradientBoostingClassifier()gbk.fit(x_train, y_train)..返回：Traceback (most recent call last):  File "<stdin>", line 1, in <module>  File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py", line 1395, in fit    X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)  File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 756, in check_X_y    estimator=estimator)  File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 527, in check_array    array = np.asarray(array, dtype=dtype, order=order)  File "/home/sj/anaconda3/lib/python3.7/site-packages/numpy/core/numeric.py", line 501, in asarray    return array(a, dtype, copy=False, order=order)ValueError: could not convert string to float: 'Baby'幫助將不勝感激。我對 DS 很陌生。