What is Feature Extraction?
Feature Extraction is the process of transforming raw data into a set of features that can be used as input to a machine learning algorithm. It involves selecting the most relevant and informative attributes from the data that can effectively represent the underlying patterns and relationships. Feature extraction can be done using various techniques, such as dimensionality reduction, feature selection, and feature engineering.
Example of Feature Extraction using PCA in Python:
from sklearn.decomposition import PCA
import numpy as np
# Generate sample data
data = np.random.rand(100, 10)
# Perform PCA for feature extraction
pca = PCA(n_components=3)
reduced_data = pca.fit_transform(data)
print("Reduced data shape:", reduced_data.shape)
In this example, we use the scikit-learn library to perform principal component analysis (PCA) for feature extraction, reducing the dimensionality of the data from 10 to 3.
Resources
To learn more about Feature Extraction, you can explore the following resources:
