根据心情补充，语言都是Python

hash，把所有的文本转化成数字

from sklearn.preprocessing import LabelEncoderfor c in train.columns:    if train[c].dtype == 'object':        lbl = LabelEncoder()        lbl.fit(list(train[c].values) + list(test[c].values))        train[c] = lbl.transform(list(train[c].values))        test[c] = lbl.transform(list(test[c].values))

Xgboost训练

'''Train the xgb model then predict the test data'''

xgb_params = {    'n_trees': 520,     'eta': 0.0045,    'max_depth': 4,    'subsample': 0.93,    'objective': 'reg:linear',     'eval_metric': 'rmse',    'base_score': y_mean, # base prediction = mean(target)    'silent': 1}# NOTE: Make sure that the class is labeled 'class' in the data filedtrain = xgb.DMatrix(train.drop('y', axis=1), y_train)dtest = xgb.DMatrix(test)num_boost_rounds = 1250# train modelmodel = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round=num_boost_rounds)y_pred = model.predict(dtest)

OneHot矩阵转换

enc = OneHotEncoder(handle_unknown='ignore')enc=enc.fit(pd.concat([X[categorical],X_test[categorical]]))X_cat_sparse=enc.transform(X[categorical])X_test_cat_sparse=enc.transform(X_test[categorical])