How would you apply Dimensionality Reduction techniques like PCA to improve the performance of a classification model in Scikit-learn

Question

With the help of code can you tell me How would you apply Dimensionality Reduction techniques like PCA to improve the performance of a classification model in Scikit-learn?

Answer 1

PCA reduces dimensionality by transforming features into principal components, improving classification performance by reducing noise and overfitting.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses PCA to reduce feature space from four dimensions to two.
• Reduces noise and simplifies model training while retaining key variance.
• Evaluates performance after dimensionality reduction.

Hence, applying PCA improves classification efficiency by reducing dimensionality, minimizing overfitting, and speeding up model training without significant loss of information.