plot_feature_importances with examples

An example showing the plot_feature_importances function used by a scikit-learn classifier.

# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause

from sklearn.datasets import (
  make_classification,
  load_breast_cancer as data_2_classes,
  load_iris as data_3_classes,
  load_digits as data_10_classes,
)
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_predict

import numpy as np; np.random.seed(0)  # reproducibility
# importing pylab or pyplot
import matplotlib.pyplot as plt

# Import scikit-plot
import scikitplot as sp

# Load the data
X, y = data_3_classes(return_X_y=True, as_frame=False)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)

# Create an instance of the LogisticRegression
model = RandomForestClassifier(random_state=0).fit(X_train, y_train)

# Plot!
ax, features = sp.estimators.plot_feature_importances(
  model,
  feature_names=['petal length', 'petal width', 'sepal length', 'sepal width'],
);

# Adjust layout to make sure everything fits
plt.tight_layout()

# Save the plot with a filename based on the current script's name
# sp.api._utils.save_plot()

# Display the plot
plt.show(block=True)

References

The use of the following functions, methods, classes and modules is shown in this example:

• https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html

Tags: model-type: classification model-workflow: model evaluation plot-type: bar level: beginner purpose: showcase

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