What is dimension reduction in machine learning?

What is dimension reduction in machine learning?

Dimension reduction is a technique used to reduce the number of features in a dataset while preserving its essential information.

Why is dimension reduction important?

It's important because it helps to simplify models, reduce computation time, and mitigate the curse of dimensionality.

Can you give me examples of dimension reduction techniques?

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

How do I implement PCA in Python?

You can use the sklearn library. First, import PCA, then fit it to your data and transform it. Here's a quick example: from sklearn.decomposition import PCA.