模型的评估

前面我们已经实现了七种模型，接下来我们分别会对这七种进行评估，主要通过auccuracy，precision，recall，F1-score，auc。最后画出各个模型的roc曲线

接下来分别看看各个评分的意义

accuracy(准确率)

对于给定的测试数据集，分类器正确分类的样本数与总样本数之比。也就是损失函数是0-1损失时测试数据集上的准确率。比如有100个数据，其中有70个正类，30个反类。现在分类器分为50个正类，50个反类，也就是说将20个正类错误的分为了反类。准确率为80/100 = 0.8

precision(精确率)

表示被”正确被检索的item(TP)"占所有"实际被检索到的(TP+FP)"的比例.，这个指标越高，就表示越整齐不混乱。比如还是上述的分类中。在分为反类中有30个分类正确。那么精确率为30/50 = 0.6

recall(召回率)

所有"正确被检索的item(TP)"占所有"应该检索到的item(TP+FN)"的比例。在上述的分类中正类的召回率为50/70 = 0.71。一般情况下准确率高、召回率就低，召回率低、准确率高

F1-score

统计学中用来衡量二分类模型精确度的一种指标。它同时兼顾了分类模型的准确率和召回率。F1分数可以看作是模型准确率和召回率的一种加权平均，它的最大值是1，最小值是0。

auc

ROC曲线下与坐标轴围成的面积

模型评估

先导入所需要的包

import pandas as pd
import numpy as py
import matplotlib.pyplot as plt
from xgboost import XGBClassifier
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve,auc
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from lightgbm import LGBMClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm

data_all = pd.read_csv('D:\data_all.csv',encoding ='gbk')

X = data_all.drop(['status'],axis = 1)
y = data_all['status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=2018)
#数据标准化
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

接下定义一个函数实现了评价的方法以及画出了roc曲线

def assess(y_pre, y_pre_proba):
    acc_score = accuracy_score(y_test,y_pre)
    pre_score = precision_score(y_test,y_pre)
    recall = recall_score(y_test,y_pre)
    F1 = f1_score(y_test,y_pre)
    auc_score = roc_auc_score(y_test,y_pre_proba)
    fpr, tpr, thresholds = roc_curve(y_test,y_pre_proba)
    plt.plot(fpr,tpr,'b',label='AUC = %0.4f'% auc_score)
    plt.plot([0,1],[0,1],'r--',label= 'Random guess')
    plt.legend(loc='lower right')
    plt.title('ROCcurve')
    plt.xlabel('false positive rate')
    plt.ylabel('true positive rate')
    plt.show()

接着我们分别对这七种模型进行评估以及得到的roc曲线图

#LR
lr = LogisticRegression(random_state = 2018)
lr.fit(X_train, y_train)
pre_lr = lr.predict(X_test)
pre_porba_lr = lr.predict_proba(X_test)[:,1]
assess(pre_lr,pre_porba_lr)

#DecisionTree
dt = DecisionTreeClassifier(random_state = 2018)
dt.fit(X_train , y_train)
pre_dt = dt.predict(X_test)
pre_proba_dt = dt.predict_proba(X_test)[:,1]
assess(pre_dt,pre_proba_dt)

#SVM
svc = svm.SVC(random_state = 2018)
svc.fit(X_train , y_train)
pre_svc = svc.predict(X_test)
pre_proba_svc = svc.decision_function(X_test)
assess(pre_svc,pre_proba_svc)

#RandomForest
rft = RandomForestClassifier()
rft.fit(X_train,y_train)
pre_rft = rft.predict(X_test)
pre_proba_rft = rft.predict_proba(X_test)[:,1]
assess(pre_rft,pre_proba_rft)

#GBDT
gb = GradientBoostingClassifier()
gb.fit(X_train,y_train)
pre_gb = gb.predict(X_test)
pre_proba_gb = gb.predict_proba(X_test)[:,1]
assess(pre_gb,pre_proba_gb)

#XGBoost
xgb_c = XGBClassifier()
xgb_c.fit(X_train,y_train)
pre_xgb = xgb_c.predict(X_test)
pre_proba_xgb = xgb_c.predict_proba(X_test)[:,1]
assess(pre_xgb,pre_proba_xgb)

#LightGBM
lgbm_c = LGBMClassifier()
lgbm_c.fit(X_train,y_train)
pre_lgbm = lgbm_c.predict(X_test)
pre_proba_lgbm = lgbm_c.predict_proba(X_test)[:,1]
assess(pre_lgbm,pre_proba_lgbm)