Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to find the importance of the features for a logistic regression model** **in Python**. So Here I am Explain to you all the possible Methods here.

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Table of Contents

## How to find the importance of the features for a logistic regression model?

**How to find the importance of the features for a logistic regression model?**One of the simplest options to get a feeling for the “influence” of a given parameter in a linear classification model (logistic being one of those), is to consider the magnitude of its coefficient times the standard deviation of the corresponding parameter in the data.

**find the importance of the features for a logistic regression model**One of the simplest options to get a feeling for the “influence” of a given parameter in a linear classification model (logistic being one of those), is to consider the magnitude of its coefficient times the standard deviation of the corresponding parameter in the data.

## Method 1

One of the simplest options to get a feeling for the “influence” of a given parameter in a linear classification model (logistic being one of those), is to consider the magnitude of its coefficient times the standard deviation of the corresponding parameter in the data.

Consider this example:

import numpy as np from sklearn.linear_model import LogisticRegression x1 = np.random.randn(100) x2 = 4*np.random.randn(100) x3 = 0.5*np.random.randn(100) y = (3 + x1 + x2 + x3 + 0.2*np.random.randn()) > 0 X = np.column_stack([x1, x2, x3]) m = LogisticRegression() m.fit(X, y) # The estimated coefficients will all be around 1: print(m.coef_) # Those values, however, will show that the second parameter # is more influential print(np.std(X, 0)*m.coef_)

An alternative way to get a similar result is to examine the coefficients of the model fit on standardized parameters:

m.fit(X / np.std(X, 0), y) print(m.coef_)

Note that this is the most basic approach and a number of other techniques for finding feature importance or parameter influence exist (using p-values, bootstrap scores, various “discriminative indices”, etc).

**Conclusion**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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