How can I visualize my machine learning model using scikit-learn?

How can I visualize my machine learning model using scikit-learn?

You can use libraries like Matplotlib and Seaborn along with scikit-learn to visualize your models. Would you like a code example?

Yes, please provide a code example for visualizing a decision tree.

Sure! Here's a simple example:

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
import matplotlib.pyplot as plt

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target

# Train a decision tree classifier
clf = DecisionTreeClassifier()
clf.fit(X, y)

# Visualize the tree
plt.figure(figsize=(12,8))
tree.plot_tree(clf, filled=True)
plt.show()

This will display a visual representation of the decision tree.

That looks great! Can I also visualize feature importance?

Absolutely! You can visualize feature importance using a bar chart. Here's how:

import numpy as np
import matplotlib.pyplot as plt

# Get feature importances
importances = clf.feature_importances_
indices = np.argsort(importances)[::-1]

# Plot the feature importances
plt.figure(figsize=(10,6))
plt.title('Feature Importances')
plt.bar(range(X.shape[1]), importances[indices], align='center')
plt.xticks(range(X.shape[1]), iris.feature_names, rotation=90)
plt.xlim([-1, X.shape[1]])
plt.show()

This will show you which features are most important for your model.