混肴矩阵
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
iris = load_iris()
clf = LogisticRegression()
clf.fit(iris.data,iris.target)
predicted = clf.predict(iris.data)
# 建立逻辑回归模型
sum(iris.target == predicted)/len(iris.target) # 计算准确率
from sklearn.metrics import accuracy_score
accuracy_score(iris.target,predicted)
# 使用sklearn内置accuracy_score计算准确率
# 注意，准确率并没有多大意义
from sklearn.metrics import confusion_matrix
m=confusion_matrix(iris.target,predicted)
# 得到逻辑回归模型混肴矩阵
%pylab inline
import seaborn
seaborn.heatmap(m) # 产生可视化混肴矩阵
from sklearn.metrics import classification_report
print(classification_report(iris.target,predicted))
# 分类报告，得到分类结果的准确率，召回率，F1，判断模型好坏
交叉验证
Holdout验证
随机选取大部分数据作训练数据集，剩余数据做验证数据集
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
iris = load_iris()
X = iris.data
y = iris.target # 依然使用自带dataset中的iris数据
from sklearn.model_selection import train_test_split
train_X,test_X,train_y,test_y=train_test_split(X,y,test_size = 0.33,random_state =123)
# train_test_split 将数据X，y分成训练数据和验证数据，test_size 是验证数据集占总数据比例，random_state随便输，不同的值会产生不同数据集
clf = DecisionTreeClassifier()
clf.fit(train_X,train_y) # 使用训练数据集训练决策树模型
from sklearn.metrics import accuracy_score
predicted = clf.predict(test_X)
accuracy_score(test_y,predicted) # 计算模型对验证数据集的准确率
from sklearn.metrics import confusion_matrix
m = confusion_matrix(test_y,predicted) #模型对验证数据集的混肴矩阵
print(m)
交叉验证
将数据随机分成N份，将N-1份作为训练数据，1份作为验证数据，重复N次后平均
from sklearn.model_selection import KFold
kf = KFold(n_splits=10) #将数据分成10份
acc=[]
for train,test in kf.split(X):
train_X,test_X,train_y,test_y = X[train],X[test],y[train],y[test]
clf= DecisionTreeClassifier()
clf.fit(train_X,train_y)
predicted = clf.predict(test_X)
acc.append(accuracy_score(test_y,predicted))
print(sum(acc)/len(acc)) #打印出验证的准确率的平均值
另一种方法
from sklearn.model_selection import cross_val_score
acc = cross_val_score(clf,X=iris.data,y=iris.target,cv=10) #cv=10 表示做10次交叉验证
# acc 为10次交叉验证准确率的array
print(acc.mean())
留一验证
N-1个数据做训练，1个数据做验证，重复N次（相当与交叉验证分成N（=数据量）份）
from sklearn.model_selection import LeaveOneOut
res = []
loo = LeaveOneOut()
for train,test in loo.split(X):
train_X,test_X,train_y,test_y = X[train],X[test],y[train],y[test]
clf= DecisionTreeClassifier()
clf.fit(train_X,train_y)
predicted = clf.predict(test_X)
res.extend((predicted==test_y).tolist())
sum(res)
ROC曲线评价分类模型
生成ROC曲线
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn import preprocessing
iris = load_iris()
X = iris.data[50:150,] # ROC曲线使用二维混肴矩阵，选择2个分类的数据
le = preprocessing.LabelEncoder()
y = le.fit_transform(iris.target[50:150]) # 选择的数据target值为1和2，使用preprocessing转换成0和1
from sklearn.model_selection import train_test_split
train_X,test_X,train_y,test_y=train_test_split(X,y,test_size = 0.33,random_state =123)
clf= DecisionTreeClassifier()
clf.fit(train_X,train_y)
probas_ = clf.fit(train_X,train_y).predict_proba(test_X)
from sklearn.metrics import roc_curve,auc
fpr,tpr,thresholds = roc_curve(test_y,probas_[:,1]) #生成false positive rate 和true positive rate
import matplotlib.pyplot as plt
plt.plot(fpr,tpr,label='ROC curve')
plt.plot([0,1],[0,1],'k--')
plt.xlim([0.0,1.0])
plt.ylim([0.0,1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc='lower right')
plt.show()
ROC Curve
计算auc（areas under curve)
auc越大模型越准确
from sklearn.metrics import auc
roc_auc = auc(fpr,tpr)
print('Area under the curve:{}'.format(roc_auc))
不同模型ROC曲线对比
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
clf1 = DecisionTreeClassifier()
clf1.fit(train_X,train_y)
clf2 = SVC(probability =True)
clf2.fit(train_X,train_y)
clf3 = LogisticRegression()
clf3.fit(train_X,train_y)
clf4 = RandomForestClassifier()
clf4.fit(train_X,train_y)
from sklearn.metrics import roc_curve,auc
plt.figure(figsize=[20,10])
for clf,title in zip([clf1,clf2,clf3,clf4],['Decision Tree','SVM','LogisticRegression','RandomForest']):
probas_ = clf.fit(train_X,train_y).predict_proba(test_X)
fpr,tpr,thresholds = roc_curve(test_y,probas_[:,1])
plt.plot(fpr,tpr,label='%s-AUC:%.2f'%(title,auc(fpr,tpr)))
plt.plot([0,1],[0,1],'k--')
plt.xlim([0.0,1.0])
plt.ylim([0.0,1.0])
plt.xlabel('False Positive Rate',fontsize=20)
plt.ylabel('True Positive Rate',fontsize=20)
plt.title('ROC Curve',fontsize=20)
plt.legend(loc='lower right',fontsize=20)
plt.show()
ROC曲线对比
按模型中维度重要性排序
import numpy as np
columns = np.array(iris.feature_names) # 将feature_names由列表变为array
importance = columns[clf1.feature_importances_.argsort()[::-1]]
# clf1.feature_importances_ 生成各个特征的重要性
# argsort() 获得array中的值按从小到大在array中的位置的array。
# [::-1]将上面的array逆排序
# columns[clf1.feature_importances_.argsort()[::-1]] 得到按照importance从大到小排序的array
print(importance)
#特征维度重要性排序可视化
import matplotlib.pyplot as plt
featur_importance = clf1.feature_importances_
plt.title('Feature Importance')
plt.bar(range(0,len(importance)),feature_importance[feature_importance.argsort()[::-1]])
plt.xticks(range(0,len(importance)),importance,rotation=90)
plt.show()
特征维度重要性排序
