[scikit-learn] Inconsistent Logistic Regression fit results

Chris Cameron chris at upnix.com
Mon Aug 15 17:27:03 EDT 2016

Hi all,

Using the same X and y values sklearn.linear_model.LogisticRegression.fit() is providing me with inconsistent results.

The documentation for sklearn.linear_model.LogisticRegression states that "It is thus not uncommon, to have slightly different results for the same input data.” I am experiencing this, however the fix of using a smaller “tol” parameter isn’t providing me with consistent fit.

The code I’m using:

def log_run(logreg_x, logreg_y):
    logreg_x['pass_fail'] = logreg_y
    df_train, df_test = train_test_split(logreg_x, random_state=0)
    y_train = df_train.pass_fail.as_matrix()
    y_test = df_test.pass_fail.as_matrix()
    log_reg_fit = LogisticRegression(class_weight='balanced',tol=0.000000001).fit(df_train, y_train)
    predicted = log_reg_fit.predict(df_test)
    accuracy = accuracy_score(y_test, predicted)
    kappa = cohen_kappa_score(y_test, predicted)
    return [kappa, accuracy]

I’ve gone out of my way to be sure the test and train data is the same for each run, so I don’t think there should be random shuffling going on.

Example output:
log_run(df_save, y)
Out[32]: [0.027777777777777728, 0.53333333333333333]

log_run(df_save, y)
Out[33]: [0.027777777777777728, 0.53333333333333333]

log_run(df_save, y)
Out[34]: [0.11347517730496456, 0.58333333333333337]

log_run(df_save, y)
Out[35]: [0.042553191489361743, 0.55000000000000004]

log_run(df_save, y)
Out[36]: [-0.07407407407407407, 0.51666666666666672]

log_run(df_save, y)
Out[37]: [0.042553191489361743, 0.55000000000000004]

A little information on the problem DataFrame:
Out[40]: 240

Out[41]: 18

If I omit this particular column the Kappa no longer fluctuates:

0    0.026316
1    0.333333
2    0.015152
3    0.010526
4    0.125000
Name: abc, dtype: float64

Does anyone have ideas on how I can figure this out? Is there some randomness/shuffling still going on I missed?


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