利用随机森林、GBDT、xgboost、LightGBM计算准确率和auc

最新推荐文章于 2025-05-27 16:36:02 发布

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本文对比了随机森林、GBDT、XGBoost和LightGBM在某数据集上的表现，通过计算准确率和AUC值，分析了各模型的优劣。结果显示，GBDT在准确率和AUC值上表现最佳。

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利用随机森林、GBDT、xgboost、LightGBM计算准确率和auc

用到的模块

import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn import metrics
from sklearn.metrics import accuracy_score,roc_auc_score
from xgboost.sklearn import XGBClassifier

读取数据集

data_all = pd.read_csv('/home/infisa/wjht/project/DataWhale/data_all.csv', encoding='gbk')

划分数据集和测试集

features = [x for x in data_all.columns if x not in ['status']]
X = data_all[features]
y = data_all['status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2018)

构建模型　计算准确率

forest=RandomForestClassifier(n_estimators=100,random_state=2018) #　随机森林
forest.fit(X_train,y_train)
forest_y_score=forest.predict_proba(X_test)
# print(forest_y_score[:,1])
forest_score=forest.score(X_test,y_test) #准确率
# print('forest_score:',forest_score)
'ranfor_score:0.7820602662929222'

Gbdt=GradientBoostingClassifier(random_state=2018) #CBDT
Gbdt.fit(X_train,y_train)
Gbdt_score=Gbdt.score(X_train,y_train) #准确率
# print('Gbdt_score:',Gbdt_score)
'Gbdt_score:0.8623384430417794'

Xgbc=XGBClassifier(random_state=2018)  #Xgbc
Xgbc.fit(X_train,y_train)
y_xgbc_pred=Xgbc.predict(X_test)
Xgbc_score=accuracy_score(y_test,y_xgbc_pred) #准确率
# print('Xgbc_score:',Xgbc_score)
'Xgbc_score:0.7855641205325858'

gbm=lgb.LGBMClassifier(random_state=2018)  #lgb
gbm.fit(X_train,y_train)
y_gbm_pred=gbm.predict(X_test)
gbm_score=accuracy_score(y_test,y_gbm_pred)  #准确率
# print('gbm_score:',gbm_score)
'gbm_score:0.7701471618780659'

计算auc

y_test_hot = label_binarize(y_test,classes =(0, 1)) # 将测试集标签数据用二值化编码的方式转换为矩阵
Gbdt_y_score = Gbdt.decision_function(X_test) # 得到Gbdt预测的损失值
forest_fpr,forest_tpr,forest_threasholds=metrics.roc_curve(y_test_hot.ravel(),forest_y_score[:,1].ravel()) # 计算ROC的值,forest_threasholds为阈值
Gbdt_fpr,Gbdt_tpr,Gbdt_threasholds=metrics.roc_curve(y_test_hot.ravel(),Gbdt_y_score.ravel()) # 计算ROC的值,Gbdt_threasholds为阈值

forest_auc=metrics.auc(forest_fpr,forest_tpr) #Gbdt_auc值
# print('forest_auc',forest_auc)
'forest_auc 0.7491366989035293'

Gbdt_auc=metrics.auc(Gbdt_fpr,Gbdt_tpr) #Gbdt_auc值
# print('Gbdt_auc:',Gbdt_auc)
'Gbdt_auc:0.7633094425839567'

Xgbc_auc=roc_auc_score(y_test,y_xgbc_pred) #Xgbc_auc值
# print('Xgbc_auc:',Xgbc_auc)
'Xgbc_auc:0.6431606209508309'

gbm_auc=roc_auc_score(y_test,y_gbm_pred) #gbm_auc值
# print('gbm_auc:',gbm_auc)
'gbm_auc:0.6310118097503468'