Dimensionality reduction in data science

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.

1. What is dimensionality reduction example?
2. What are dimensionality reduction and its benefits in data science?
3. What is dimensionality reduction in data preprocessing?

What is dimensionality reduction example?

We can apply a dimensionality reduction method (usually PCA) to covert high-dimensional data into 2 or 3-dimensional data that can be plotted in a 2D or 3D plot. As an example for this, consider the breast_cancer dataset that has 30 variables. So, the dimensionality of the data is 30.

What are dimensionality reduction and its benefits in data science?

Advantages of dimensionality reduction

It reduces the time and storage space required. The removal of multicollinearity improves the interpretation of the parameters of the machine learning model. It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D. Reduce space complexity.

What is dimensionality reduction in data preprocessing?

Dimensionality reduction is the process of reducing the number of random variables or attributes under consideration. High-dimensionality data reduction, as part of a data pre-processing-step, is extremely important in many real-world applications.