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 (
load_iris as data_3_classes,
)
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
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"],
save_fig=True,
save_fig_filename="",
# overwrite=True,
add_timestamp=True,
# verbose=True,
)
References
The use of the following functions, methods, classes and modules is shown in this example:
Total running time of the script: (0 minutes 0.390 seconds)
Related examples
