scree plot in sklearn - Python

Scree plots are used to visualize the amount of variation explained by each principal component in a principal component analysis (PCA). They help to determine the optimal number of components to retain for further analysis.

In sklearn, the scree plot can be generated by examining the explained variance ratio of each principal component. The explained variance ratio tells us how much of the variation in the data is explained by each principal component. We can plot the cumulative sum of the explained variance ratios against the number of components to determine the optimal number of components to retain.

Here's an example code snippet:

from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

# Load iris dataset
iris = load_iris()

# Run PCA
pca = PCA().fit(iris.data)

# Compute cumulative sum of explained variance ratios
cumulative_var_ratio = np.cumsum(pca.explained_variance_ratio_)

# Plot scree plot
plt.plot(range(1, len(iris.feature_names) + 1), cumulative_var_ratio, marker='o')
plt.xlabel('Number of components')
plt.ylabel('Cumulative explained variance ratio')
plt.title('Scree plot of iris dataset')
plt.show()

This code loads the iris dataset, runs PCA, computes the cumulative sum of explained variance ratios, and plots the scree plot. The resulting plot should look like this:

In this case, we can see that the first two principal components explain a significant amount of variation in the data, and so we might decide to retain only those two components for further analysis.

Overall, scree plots are a useful tool for determining the optimal number of components to retain in PCA. They can help to reduce the dimensionality of high-dimensional datasets and make them more manageable for downstream analyses.