Principal Component Regression Calculator

Enter your data below as comma-separated values with one data point per line. For multiple features, use format x1,x2,...,y. Specify the number of principal components to use, and the calculator will perform PCR and provide statistics about the model quality and explained variance.

Data Input

Format: Each line should contain 1 feature values followed by 1 target value, all comma-separated.

Model Parameters

15

Principal Component Regression (PCR)

Principal Component Regression combines Principal Component Analysis (PCA) with linear regression. It first reduces the dimensionality of the data using PCA and then performs regression on the principal components.

How It Works

PCR works in two main steps:

  1. Dimensionality Reduction: PCA transforms the original features into a new set of uncorrelated variables called principal components, ordered by the amount of variance they explain.

  2. Regression: Linear regression is performed using the selected principal components as predictors instead of the original features.

Key parameters include:

  • Number of Components: How many principal components to retain for the regression

When to Use Principal Component Regression

Use Principal Component Regression when:

  • You have many correlated features (multicollinearity)
  • You want to reduce dimensionality while preserving most of the variance
  • You need to handle noisy data
  • You want to visualize high-dimensional data
  • You want to avoid overfitting in the presence of many features

How to Use This Calculator

Enter your data below as comma-separated values with one data point per line. For multiple features, use format x1,x2,...,y. Specify the number of principal components to use, and the calculator will perform PCR and provide statistics about the model quality and explained variance.