What is the impact of dimensionality reduction on machine learning?

What is the impact of dimensionality reduction on machine learning?

Dimensionality reduction helps improve model performance by reducing overfitting and computational cost.

Can you explain how it reduces overfitting?

By removing irrelevant features, it simplifies the model, making it less likely to learn noise from the training data.

What are some common techniques for dimensionality reduction?

Common techniques include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminant Analysis (LDA).

How do I choose the right technique for my dataset?

It depends on your data's characteristics and the specific problem. PCA is great for linear relationships, while t-SNE is better for visualizing high-dimensional data.