根據心情補充，語言都是Python

hash，把所有的文本轉化成數字

from sklearn.preprocessing import LabelEncoder for 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訓練

'''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 model model = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round=num_boost_rounds) y_pred = model.predict(dtest)

OneHot矩陣轉換

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])

轉載于:https://www.cnblogs.com/qscqesze/p/7053740.html

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