When would you use dimensionality reduction?

Dimensionality reduction is a data preparation technique performed on data prior to modeling. It might be performed after data cleaning and data scaling and before training a predictive model.

When would you reduce dimensions in your data in machine learning?

For high-dimensional datasets (i.e. with number of dimensions more than 10), dimension reduction is usually performed prior to applying a K-nearest neighbors algorithm (k-NN) in order to avoid the effects of the curse of dimensionality.

How can you reduce the size of data?

Seven Techniques for Data Dimensionality Reduction

1. Missing Values Ratio.
2. Low Variance Filter.
3. High Correlation Filter.
4. Random Forests / Ensemble Trees.
5. Principal Component Analysis (PCA).
6. Backward Feature Elimination.
7. Forward Feature Construction.

What is the need of dimensionality reduction How do you overcome this?

Dimensionality reduction can be further broken into feature selection and feature extraction. Feature selection tries to select a subset of the original features for use in the machine learning model. In this way, we could remove redundant and irrelevant features without incurring much loss of information.

What is the need of dimensionality reduction explain subset selection?

Reduction of dimensionality is the method of reducing with consideration the dimensionality of the function space by obtaining a collection of principal features. The selection of features tries to pick a subset of the original features to be used in the machine learning model.

What is the purpose of dimensionality reduction in data pre processing?

Dimensionality reduction simply refers to the process of reducing the number of attributes in a dataset while keeping as much of the variation in the original dataset as possible. It is a data preprocessing step meaning that we perform dimensionality reduction before training the model.