Dimensionality Reduction

TL;DR

Reduce the number of features or dimensions in a dataset to improve model performance and interpretability.
Helps reduce overfitting by eliminating redundant or irrelevant information.
Common approaches include Principal Component Analysis (PCA) and feature selection (filter, wrapper, and embedded methods).

Definition

Dimensionality reduction is a technique used in machine learning to reduce the number of features or dimensions in a dataset. This is done to improve the performance of the model, reduce overfitting, and make the data easier to interpret and analyze.

Explanation

Dimensionality reduction transforms a high-dimensional dataset into a lower-dimensional representation while preserving as much relevant information as possible. Methods vary in approach:

Principal Component Analysis (PCA) projects data onto a set of orthogonal axes called principal components. These components are computed to capture the maximum amount of variation in the data, and a subset of top components is selected to form the lower-dimensional space.
Feature selection chooses a subset of existing features based on their relevance or importance to the task. Selection methods include:
- Filter methods: use statistical measures to evaluate and rank features.
- Wrapper methods: use a model’s performance to evaluate feature subsets.
- Embedded methods: use the learning algorithm itself to evaluate and select features.

Examples

Principal Component Analysis (PCA)

In PCA, a high-dimensional dataset is transformed into a lower-dimensional space by projecting the data onto a set of orthogonal axes or principal components. These principal components are calculated such that they capture the maximum amount of variation in the data.

For instance, consider a dataset with 10 features or dimensions. PCA can be used to reduce the dimensionality of this dataset to, say, 5 dimensions. This is done by computing the principal components and selecting the top 5 components that capture the most variation in the data. The original 10-dimensional dataset is then transformed into a 5-dimensional dataset, which can be used to train a machine learning model.

Feature Selection

In feature selection, certain features in the dataset are selected based on their relevance or importance for the task at hand. For instance, consider a dataset with 100 features, out of which only 10 features are relevant for predicting the target variable. In this case, feature selection can be used to select only the 10 relevant features, thereby reducing the dimensionality of the dataset from 100 to 10 dimensions.

This can be done using various methods such as filter methods, wrapper methods, and embedded methods. Filter methods use statistical measures to evaluate the importance of each feature and select the top features based on these measures. Wrapper methods use the performance of the machine learning model on the dataset to evaluate the importance of each feature and select the top features. Embedded methods use the learning algorithm itself to evaluate the importance of each feature and select the top features.

Use cases

Improve the performance of machine learning models.
Reduce overfitting.
Make data easier to interpret and analyze.

Principal Component Analysis (PCA)
Principal components
Feature selection
Filter methods
Wrapper methods
Embedded methods