sklearn random forest feature importance
import pandas as pd
forest_importances = pd.Series(importances, index=feature_names)
fig, ax = plt.subplots()
forest_importances.plot.bar(yerr=std, ax=ax)
ax.set_title("Feature importances using MDI")
ax.set_ylabel("Mean decrease in impurity")
fig.tight_layout()sklearn random forest feature importance
import time
import numpy as np
start_time = time.time()
importances = forest.feature_importances_
std = np.std([
tree.feature_importances_ for tree in forest.estimators_], axis=0)
elapsed_time = time.time() - start_time
print(f"Elapsed time to compute the importances: "
f"{elapsed_time:.3f} seconds")sklearn random forest feature importance
from sklearn.ensemble import RandomForestClassifier
feature_names = [f'feature {i}' for i in range(X.shape[1])]
forest = RandomForestClassifier(random_state=0)
forest.fit(X_train, y_train)sklearn random forest feature importance
print(__doc__)
import matplotlib.pyplot as pltsklearn random forest feature importance
RandomForestClassifier(random_state=0)sklearn random forest feature importance
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(
n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
n_repeated=0, n_classes=2, random_state=0, shuffle=False)
X_train, X_test, y_train, y_test = train_test_split(
X, y, stratify=y, random_state=42)Copyright © 2021 Codeinu
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