Dimensionality Reduction

Dimensionality reduction is a technique used in data science to reduce the number of input variables in a dataset.

How It Works

Dimensionality reduction works by creating new combinations of attributes (Principal Component Analysis, PCA) or by finding attributes that are redundant (like in Autoencoders) and removing them from the dataset.

Benefits

• Efficiency: It can make the data analysis or model training process more efficient.
• Overfitting Prevention: It can help to prevent overfitting by simplifying models.

Limitations

• Information Loss: It can lead to a loss of information if not done carefully.
• Interpretability: The new variables after dimensionality reduction can sometimes be less interpretable.

Features

• Variable Reduction: It reduces the number of input variables in a dataset.
• Data Simplification: It simplifies the data while retaining its structure and usefulness.

Use Cases

• High Dimensional Data: It’s useful when dealing with high dimensional data.
• Visualization: It’s used when trying to visualize high-dimensional data.

Self-Correlation and Removal of Highly Correlated Features

Running self-correlation on all features and removing highly correlated features is a method used to reduce multicollinearity in the dataset.

How It Works

This method works by calculating the correlation between all pairs of features. If the correlation between a pair of features exceeds a certain threshold, one of the features is removed.