Yellowbrick for Visualization of Tree Models
Yellowbrick is a Python library for visualizing the model performance. To visualize a decision tree using Yellowbrick, we can use the ClassPredictionError visualizer.
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from yellowbrick.classifier import ClassPredictionError
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)
visualizer = ClassPredictionError(clf, classes=iris.target_names)
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.show()
Output:
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