使用交叉验证评估Logistic回归 [英] Evaluating Logistic regression with cross validation

问题描述

我想使用交叉验证来测试/训练我的数据集，并在整个数据集中而不是仅在测试集(例如25％)上评估逻辑回归模型的性能.

I would like to use cross validation to test/train my dataset and evaluate the performance of the logistic regression model on the entire dataset and not only on the test set (e.g. 25%).

这些概念对我来说是全新的，并且不确定是否正确.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.

These concepts are totally new to me and am not very sure if am doing it right. I would be grateful if anyone could advise me on the right steps to take where I have gone wrong. Part of my code is shown below.

此外，如何在与当前图形相同的图形上绘制"y2"和"y3"的ROC?

Also, how can I plot ROCs for "y2" and "y3" on the same graph with the current one?

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() 

推荐答案

您几乎完全正确. 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: 它将您的数据集分为n个折叠，并且在每次迭代中，将其中一个折叠留作测试集，并在其余折叠(n-1个折叠)上训练模型.因此，最后您将获得整个数据的预测.

You got it almost right. cross_validation.cross_val_predict gives you predictions for the entire dataset. You just need to remove logreg.fit earlier in the code. Specifically, what it does is the following: It divides your dataset in to n folds and in each iteration it leaves one of the folds out as the test set and trains the model on the rest of the folds (n-1 folds). So, in the end you will get predictions for the entire data.

让我们用虹膜sklearn中的内置数据集之一对此进行说明.该数据集包含150个具有4个特征的训练样本. iris['data']是X，iris['target']是y

Let's illustrate this with one of the built-in datasets in sklearn, iris. This dataset contains 150 training samples with 4 features. iris['data'] is X and iris['target'] is 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:

To get predictions on the entire set with cross validation you can do the following:

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.您所需要做的就是这个:

So, back to your code. All you need is this:

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，可以遵循本教程会为您提供以下内容:

For plotting ROC in multi-class classification, you can follow this tutorial which gives you something like the following:

通常，sklearn具有非常好的教程和文档.我强烈建议阅读他们的关于cross_validation的教程.

In general, sklearn has very good tutorials and documentation. I strongly recommend reading their tutorial on cross_validation.

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