模型融合目標
對於多次調參完成的模型進行模型融合
內容介紹
- 簡單加權融合:
1) 迴歸:(分類概率):算術平均融合(Arithmetic mean),幾何平均融合(Geometric mean);
2) 分類:投票(Voting)
3)綜合:排序融合(Rank averaging),log融合
4)Stacking融合 - 構建錯層模型,利用預測結果再擬合
- 多樹的提升方法
簡單加權融合
利用算數平均,重新生成一個模型結果
def Weighted_method(test_pre1,test_pre2,test_pre3,w=[1/3,1/3,1/3]):
Weighted_result = w[0]*pd.Series(test_pre1)+w[1]*pd.Series(test_pre2)+w[2]*pd.Series(test_pre3)
return Weighted_result
## Init the Weight
w = [0.3,0.4,0.3]
## 測試驗證集準確度
val_pre = Weighted_method(val_lgb,val_xgb,val_gbdt,w)
MAE_Weighted = mean_absolute_error(y_val,val_pre)
print('MAE of Weighted of val:',MAE_Weighted)
## 預測數據部分
subA = Weighted_method(subA_lgb,subA_xgb,subA_gbdt,w)
Stacking 融合
利用第二層的模型重新跑一次第一層生成的訓練集、驗證集和測試集進行預測。
## 第一層
train_lgb_pred = model_lgb.predict(x_train)
train_xgb_pred = model_xgb.predict(x_train)
train_gbdt_pred = model_gbdt.predict(x_train)
Strak_X_train = pd.DataFrame()
Strak_X_train['Method_1'] = train_lgb_pred
Strak_X_train['Method_2'] = train_xgb_pred
Strak_X_train['Method_3'] = train_gbdt_pred
Strak_X_val = pd.DataFrame()
Strak_X_val['Method_1'] = val_lgb
Strak_X_val['Method_2'] = val_xgb
Strak_X_val['Method_3'] = val_gbdt
Strak_X_test = pd.DataFrame()
Strak_X_test['Method_1'] = subA_lgb
Strak_X_test['Method_2'] = subA_xgb
Strak_X_test['Method_3'] = subA_gbdt
## 第二層
model_lr_Stacking = build_model_lr(Strak_X_train,y_train)
## 訓練集
train_pre_Stacking = model_lr_Stacking.predict(Strak_X_train)
print('MAE of Stacking-LR:',mean_absolute_error(y_train,train_pre_Stacking))
## 驗證集
val_pre_Stacking = model_lr_Stacking.predict(Strak_X_val)
print('MAE of Stacking-LR:',mean_absolute_error(y_val,val_pre_Stacking))
## 預測集
print('Predict Stacking-LR...')
subA_Stacking = model_lr_Stacking.predict(Strak_X_test)