To try the examples in the browser:

# Example code to try:
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.ensemble import RandomForestClassifier

#import matplotlib.pyplot as plt
import scikitplot as skplt

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

# Create and train the model
model = RandomForestClassifier().fit(X_train, y_train)

# Plot feature importances
ax, features = skplt.estimators.plot_feature_importances(
    model, display_bar_label=False,
    figsize=(12,5)
);
import micropip; micropip.list()