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如何提高Scikit python中逻辑回归的模型精度?

我试图用gre,gpa和rank等预测变量来预测admit变量.但是预测精度非常低(0.66).数据集如下所示. https://gist.github.com/abyalias/3de80ab7fb93dcecc565cee21bd9501a

请找到以下代码:

 In[73]: data.head(20)
 Out[73]: 

   admit  gre   gpa  rank_2  rank_3  rank_4
0      0  380  3.61     0.0     1.0     0.0
1      1  660  3.67     0.0     1.0     0.0
2      1  800  4.00     0.0     0.0     0.0
3      1  640  3.19     0.0     0.0     1.0
4      0  520  2.93     0.0     0.0     1.0
5      1  760  3.00     1.0     0.0     0.0
6      1  560  2.98     0.0     0.0     0.0

y = data['admit']
x = data[data.columns[1:]]

from sklearn.cross_validation import  train_test_split
xtrain,xtest,ytrain,ytest  = train_test_split(x,y,random_state=2)

ytrain=np.ravel(ytrain)

#modelling 
clf = LogisticRegression(penalty='l2')
clf.fit(xtrain,ytrain) …
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python regression machine-learning scikit-learn logistic-regression

11
推荐指数
1
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2万
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