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I wonder what causes some strange behavior of LogisticRegression's solvers in the following model:

  1. For some reason, all of them except liblinear predict only 0s.
  2. Their loglosses are equivalent, except for liblinear.
   from sklearn import preprocessing
   from sklearn.model_selection import train_test_split

   !wget -O ChurnData.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/ChurnData.csv
   churn_df = pd.read_csv("ChurnData.csv")
   X = np.asarray(churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip']])
   y = np.asarray(churn_df['churn'])

   X = preprocessing.StandardScaler().fit(X).transform(X)
   X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=5)

   churn_df = churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip',   'callcard', 'wireless','churn']]
   churn_df['churn'] = churn_df['churn'].astype('int')


   # Test out the differences between various solvers.
   from sklearn.linear_model import LogisticRegression

   LR = LogisticRegression(C=0.0001, solver='newton-cg').fit(X_train,y_train)
   Yhat_ps = LR.predict_proba(X_test)
   Yhat = LR.predict(X_test)

   LR2 = LogisticRegression(C=0.0001, solver='saga').fit(X_train,y_train)
   Yhat_ps2 = LR2.predict_proba(X_test)
   Yhat2 = LR2.predict(X_test)

   print(Yhat_ps - Yhat_ps2)
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  • 1
    $\begingroup$ Do you mean to strongly regularize your logistic regression? C is actually the inverse of the regularization strength, so a C of 0.0001 is equivalent to a lambda of 10000. $\endgroup$ – Demetri Pananos Nov 7 at 15:00
  • $\begingroup$ You are right, increasing C causes all the solvers to give some 1s too! Nevertheless, the differences between the loglosses of every solver except liblinear are of order -5. Why is liblinear so different? $\endgroup$ – Mixel Nov 7 at 15:58

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