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Principal Component Analysis in Python

Variance, Covariance, and the Covariance Matrix

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Note
Definition

Variance measures how much a variable deviates from its mean.

The formula for variance of a variable xx is:

Var(x)=1n∑i=1n(xi−xˉ)2\mathrm{Var}(x) = \frac{1}{n} \sum_{i=1}^n (x_i - \bar{x})^2
Note
Definition

Covariance measures how two variables change together.

The formula for Covariance of variables xx and yy is:

Cov(x,y)=1n−1∑i=1n(xi−xˉ)(yi−yˉ)\mathrm{Cov}(x, y) = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})(y_i - \bar{y})

The covariance matrix generalizes covariance to multiple variables. For a dataset XX with dd features and nn samples, the covariance matrix Σ\Sigma is a d×dd \times d matrix where each entry Σij\Sigma_{ij} is the covariance between feature ii and feature jj, computed with denominator n−1n-1 to be an unbiased estimator.


              
12345678910111213

            
import numpy as np

# Example data: 3 samples, 2 features
X = np.array([[2.5, 2.4],
              [0.5, 0.7],
              [2.2, 2.9]])

# Center the data (subtract mean)
X_centered = X - np.mean(X, axis=0)

# Compute covariance matrix manually
cov_matrix = (X_centered.T @ X_centered) / X_centered.shape[0]
print("Covariance matrix:\n", cov_matrix)

In the code above, you manually center the data and compute the covariance matrix using matrix multiplication. This matrix captures how each pair of features varies together.

question mark

Which statement accurately describes the relationship between variance, covariance, and the covariance matrix

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どのように改善できますか？

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セクション 2. 章 1

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Variance, Covariance, and the Covariance Matrix

Note
Definition

Variance measures how much a variable deviates from its mean.

The formula for variance of a variable xx is:

Var(x)=1n∑i=1n(xi−xˉ)2\mathrm{Var}(x) = \frac{1}{n} \sum_{i=1}^n (x_i - \bar{x})^2
Note
Definition

Covariance measures how two variables change together.

The formula for Covariance of variables xx and yy is:

Cov(x,y)=1n−1∑i=1n(xi−xˉ)(yi−yˉ)\mathrm{Cov}(x, y) = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})(y_i - \bar{y})

The covariance matrix generalizes covariance to multiple variables. For a dataset XX with dd features and nn samples, the covariance matrix Σ\Sigma is a d×dd \times d matrix where each entry Σij\Sigma_{ij} is the covariance between feature ii and feature jj, computed with denominator n−1n-1 to be an unbiased estimator.


              
12345678910111213

            
import numpy as np

# Example data: 3 samples, 2 features
X = np.array([[2.5, 2.4],
              [0.5, 0.7],
              [2.2, 2.9]])

# Center the data (subtract mean)
X_centered = X - np.mean(X, axis=0)

# Compute covariance matrix manually
cov_matrix = (X_centered.T @ X_centered) / X_centered.shape[0]
print("Covariance matrix:\n", cov_matrix)

In the code above, you manually center the data and compute the covariance matrix using matrix multiplication. This matrix captures how each pair of features varies together.

すべて明確でしたか？

どのように改善できますか？

フィードバックありがとうございます！

セクション 2. 章 1
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