參數解釋，后續補上。
1 # -*- coding: utf-8 -*- 2 """ 3 ############################################################################### 4 # 作者：wanglei5205 5 # 郵箱：wanglei5205@126.com 6 # 代碼：http://github.com/wanglei5205 7 # 博客：http://cnblogs.com/wanglei5205 8 # 目的：學習xgboost的XGBClassifier函數 9 # 官方API文檔：http://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.training 10 ############################################################################### 11 """ 12 ### load module 13 from sklearn import datasets 14 from sklearn.model_selection import train_test_split 15 from xgboost import XGBClassifier 16 17 ### load datasets 18 digits = datasets.load_digits() 19 20 ### data analysis 21 print(digits.data.shape) 22 print(digits.target.shape) 23 24 ### data split 25 x_train,x_test,y_train,y_test = train_test_split(digits.data, 26 digits.target, 27 test_size = 0.3, 28 random_state = 33) 29 30 ### fit model for train data 31 # fit函數參數：eval_set=[(x_test,y_test)] 評估數據集,list類型 32 # fit函數參數：eval_metric="mlogloss" 評估標准(多分類問題，使用mlogloss作為損失函數) 33 # fit函數參數：early_stopping_rounds= 10 如果模型的loss十次內沒有減小，則提前結束模型訓練 34 # fit函數參數：verbose = True True顯示，False不顯示 35 model = XGBClassifier() 36 model.fit(x_train, 37 y_train, 38 eval_set = [(x_test,y_test)], # 評估數據集 39 40 eval_metric = "mlogloss", 41 early_stopping_rounds = 10, 42 verbose = True) 43 44 ### make prediction for test data 45 y_pred = model.predict(x_test) 46 47 ### model evaluate 48 from sklearn.metrics import accuracy_score 49 accuracy = accuracy_score(y_test,y_pred) 50 print("accuarcy: %.2f%%" % (accuracy*100.0)) 51 """ 52 95.0% 53 """