Calculate Feature Importance With Python

 Code 1

(.env) [boris@fedora34server NUMPY]$ cat foresTree1.py

# decision tree for feature importance on a regression problem

from sklearn.datasets import make_regression

from sklearn.tree import DecisionTreeRegressor

from matplotlib import pyplot

# define dataset

X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, random_state=1)

# define the model

model = DecisionTreeRegressor()

# fit the model

model.fit(X, y)

# get importance

importance = model.feature_importances_

# summarize feature importance

for i,v in enumerate(importance):

        print('Feature: %0d, Score: %.5f' % (i,v))

# plot feature importance

pyplot.bar([x for x in range(len(importance))], importance)

pyplot.show()

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Code 2

(.env) [boris@fedora34server NUMPY]$ cat foresTree2.py

# decision tree for feature importance on a classification problem

from sklearn.datasets import make_classification

from sklearn.tree import DecisionTreeClassifier

from matplotlib import pyplot

# define dataset

X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, random_state=1)

# define the model

model = DecisionTreeClassifier()

# fit the model

model.fit(X, y)

# get importance

importance = model.feature_importances_

# summarize feature importance

for i,v in enumerate(importance):

        print('Feature: %0d, Score: %.5f' % (i,v))

# plot feature importance

pyplot.bar([x for x in range(len(importance))], importance)

pyplot.show()

References

https://machinelearningmastery.com/calculate-feature-importance-with-python/