What is explained variance in PCA?

What is explained variance in PCA?

Explained variance in PCA measures how much information (variance) is captured by each principal component.

Why is explained variance important in PCA?

It's important because it helps determine how many components to keep for a good representation of the data.

How do I calculate explained variance in Python?

You can calculate it using the explained_variance_ratio_ attribute from the PCA object in scikit-learn.

Can you show me a code example?

Sure! Here's a simple example:

from sklearn.decomposition import PCA
from sklearn.datasets import load_iris

# Load data
iris = load_iris()
X = iris.data

# Apply PCA
pca = PCA(n_components=2)
pca.fit(X)

# Explained variance
print(pca.explained_variance_ratio_)