Demonstrating Visualization of Tree Models
The decision tree is visualized using the plot_tree() function. The tree structure is displayed with nodes representing decisions and leaves representing class labels.
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import plot_tree
iris = load_iris()
X, y = iris.data, iris.target
clf = DecisionTreeClassifier(random_state=42)
clf.fit(X, y)
# Plot the decision tree
plt.figure(figsize=(12, 8))
plot_tree(clf, filled=True, feature_names=iris.feature_names, class_names=iris.target_names)
plt.title('Decision Tree Visualization')
plt.show()
Output:
Visualize the decision tree
Understanding Feature Importance and Visualization of Tree Models
Feature importance is a crucial concept in machine learning, particularly in tree-based models. It refers to techniques that assign a score to input features based on their usefulness in predicting a target variable. This article will delve into the methods of calculating feature importance, the significance of these scores, and how to visualize them effectively.
Table of Content
- Feature Importance in Tree Models
- Methods to Calculate Feature Importance
- 1. Decision Tree Feature Importance
- 2. Random Forest Feature Importance
- 3. Permutation Feature Importance
- Demonstrating Visualization of Tree Models
- Yellowbrick for Visualization of Tree Models
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