Obtaining summary from logistic regression(Python)

Question

model = LogisticRegression(random_state=0)
model.fit(X2, Y2)
Y2_prob=model.predict_proba(X2)[:,1]

I've built a logistic regression model on my training dataset X2 and Y2. Now is it possible for me to obtain the coefficients and p values from here? Because:

model.summary()

gives me:

AttributeError: 'LogisticRegression' object has no attribute 'summary'

Or can somebody help me suggest an alternative to obtain the important and significant features from this model? Any help will be appreciated. Thanks.

Possible duplicate of Find p-value (significance) in scikit-learn LinearRegression — Vivek Kumar
– Vivek Kumar, Commented Feb 16, 2018 at 11:14

Clock Slave · Accepted Answer · 2018-02-16 05:40:29Z

6

No. Its not possible to get the p-values from here. You can get the coefficients however by using model.coef_. If you need the p-values you'll have to use the statsmodels package. See this if you want to modify the sklearn class to get the p-values

answered Feb 16, 2018 at 5:40

Clock Slave

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Anonymous Name · Accepted Answer · 2019-01-15 18:01:37Z

0

You seem to be using an older model of LogisticRegression. model.summary2() should do the trick.

answered Jan 15, 2019 at 18:01

Anonymous Name

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2 Comments

Rylan Schaeffer Over a year ago

When I try this, I get AttributeError: 'LogisticRegression' object has no attribute 'summary2'. Can you clarify what version of sklearn you're using?

Loochie Over a year ago

summary2() method is available for LogitResults class in statsmodels.discrete.discrete_model module not for sklearn.linear_model.LogisticRegression.

Jaydeep Singh Tindori · Accepted Answer · 2020-01-12 13:11:34Z

0

You can use the following statements to fix this problem. It worked in my case.

from scipy import stats stats.chisqprob = lambda chisq, df:stats.chi2.sf(chisq, df)

answered Jan 12, 2020 at 13:11

Jaydeep Singh Tindori

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