3 Essential Ways to Calculate Feature Importance in Python | by Dario Radečić | Jan, 2021

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Probably the easiest way to examine feature importances is by examining the model’s coefficients. For example, both linear and logistic regression boils down to an equation in which coefficients (importances) are assigned to each input value.

Put simply, if an assigned coefficient is a large (negative or positive) number, it has some influence on the prediction. On the contrary, if the coefficient is zero, it doesn’t have any impact on the prediction.

Simple logic, but let’s put it to the test. We have a classification dataset, so logistic regression is an appropriate algorithm. After the model is fitted, the coefficients are stored in the coef_ property.

The following snippet trains the logistic regression model, creates a data frame in which the attributes are stored with their respective coefficients, and sorts that data frame by the coefficient in descending order:

That was easy, wasn’t it? Let’s examine the coefficients visually next. The following snippet makes a bar chart from coefficients:

Here’s the corresponding visualization:

Image 2 — Feature importances as logistic regression coefficients (image by author)

And that’s all there is to this simple technique. A take-home point is that the larger the coefficient is (in both positive and negative direction), the more influence it has on a prediction.

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