我想使用交叉验证来测试/训练我的数据集,并在整个数据集而不是仅在测试集(例如25%)上评估逻辑回归模型的性能。
这些概念对我来说是全新的,并且不确定是否做得正确。如果有人可以建议我采取正确的措施来解决我的问题,我将不胜感激。我的部分代码如下所示。
另外,如何在与当前图形相同的图形上绘制“y2”和“y3”的ROC?
谢谢
import pandas as pd
Data=pd.read_csv ('C:\\Dataset.csv',index_col='SNo')
feature_cols=['A','B','C','D','E']
X=Data[feature_cols]
Y=Data['Status']
Y1=Data['Status1'] # predictions from elsewhere
Y2=Data['Status2'] # predictions from elsewhere
from sklearn.linear_model import LogisticRegression
logreg=LogisticRegression()
logreg.fit(X_train,y_train)
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
from sklearn import metrics, cross_validation
predicted = cross_validation.cross_val_predict(logreg, X, y, cv=10)
metrics.accuracy_score(y, predicted)
from sklearn.cross_validation import cross_val_score
accuracy = cross_val_score(logreg, X, y, cv=10,scoring='accuracy')
print (accuracy)
print (cross_val_score(logreg, X, y, cv=10,scoring='accuracy').mean())
from nltk import ConfusionMatrix
print (ConfusionMatrix(list(y), list(predicted)))
#print (ConfusionMatrix(list(y), list(yexpert)))
# sensitivity:
print (metrics.recall_score(y, predicted) )
import matplotlib.pyplot as plt
probs = logreg.predict_proba(X)[:, 1]
plt.hist(probs)
plt.show()
# use 0.5 cutoff for predicting 'default'
import numpy as np
preds = np.where(probs > 0.5, 1, 0)
print (ConfusionMatrix(list(y), list(preds)))
# check accuracy, sensitivity, specificity
print (metrics.accuracy_score(y, predicted))
#ROC CURVES and AUC
# plot ROC curve
fpr, tpr, thresholds = metrics.roc_curve(y, probs)
plt.plot(fpr, tpr)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate)')
plt.show()
# calculate AUC
print (metrics.roc_auc_score(y, probs))
# use AUC as evaluation metric for cross-validation
from sklearn.cross_validation import cross_val_score
logreg = LogisticRegression()
cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()
最佳答案
你说得差不多了。 cross_validation.cross_val_predict
为您提供整个数据集的预测。您只需要在代码前面删除logreg.fit
。具体来说,它的作用如下:
它将您的数据集划分为n
折叠,并在每次迭代中将其中一个折叠留作测试集,并在其余折叠(n-1
折叠)上训练模型。因此,最后您将获得整个数据的预测。
让我们用虹膜sklearn中的一个内置数据集来说明这一点。该数据集包含150个具有4个特征的训练样本。 iris['data']
是X
,而iris['target']
是y
In [15]: iris['data'].shape
Out[15]: (150, 4)
要通过交叉验证对整个集合进行预测,您可以执行以下操作:
from sklearn.linear_model import LogisticRegression
from sklearn import metrics, cross_validation
from sklearn import datasets
iris = datasets.load_iris()
predicted = cross_validation.cross_val_predict(LogisticRegression(), iris['data'], iris['target'], cv=10)
print metrics.accuracy_score(iris['target'], predicted)
Out [1] : 0.9537
print metrics.classification_report(iris['target'], predicted)
Out [2] :
precision recall f1-score support
0 1.00 1.00 1.00 50
1 0.96 0.90 0.93 50
2 0.91 0.96 0.93 50
avg / total 0.95 0.95 0.95 150
因此,回到您的代码。您需要的是:
from sklearn import metrics, cross_validation
logreg=LogisticRegression()
predicted = cross_validation.cross_val_predict(logreg, X, y, cv=10)
print metrics.accuracy_score(y, predicted)
print metrics.classification_report(y, predicted)
要在多类分类中绘制ROC,可以遵循this tutorial,它为您提供了以下内容:
通常,sklearn具有非常好的教程和文档。我强烈建议阅读他们的tutorial on cross_validation。
关于python - 使用交叉验证评估Logistic回归,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/39163354/