Feature selection versus dimensionality reduction

Feature selection and dimensionality reduction are two crucial processes in the field of machine learning. Feature selection involves choosing a subset of relevant features from the original set to improve model performance and reduce computational cost. On the other hand, dimensionality reduction techniques aim to transform the feature space into a lower-dimensional space while preserving important information. While feature selection focuses on retaining the most informative features, dimensionality reduction methods like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) aim to capture the variability of the data in fewer dimensions. Both processes play a key role in addressing the curse of dimensionality and enhancing the effectiveness of machine learning models across various domains.
These techniques are essential for handling high-dimensional data, improving interpretability, and fostering generalization in predictive modeling tasks, ultimately contributing to more efficient and accurate decision-making processes.

Quizzes

When would feature selection be preferred over dimensionality reduction?
A) When you need to interpret the importance of individual features
B) When you need to reduce the data to two or three dimensions for plotting
C) When you want to transform the feature space to capture maximum variance
D) When you need to cluster data points

True or False: Dimensionality reduction can lead to loss of interpretability of the features.
A) True
B) False