A quick analysis on multivariate regression using Principal Component Analysis (PCA), PolynomialFeatures and R2 score to find a good and simple regression model.
Regression analysis is a powerful tool to predict variables when there is historical data to adjust a regression model. However, sometimes the data is not distributed in space in a linear manner, but rather in a polynomial fashion (quadratic, cubic or higher).
Sometimes, the data may be grouped in separate clusters. Take a 2D, dimensionality reduced, Iris dataset for instance:
A linear regression curve, fitted to Iris' linear features, will achieve a hyperplane that falls in between the two clusters of data:
A linear regression model cannot work on a setting with two features scattered in a non-linear manner, since it does not have enough variables to fit a good curve.
To overcome this situation, a good usage of the PCA method can result in a good dimensionality reduction that will generate a dataset with less features, but still being representative of the original data. However, simplifying the data may not be enough to get a simple model that fits well to the data. PolynomialFeatures (scikit-learn), on the other hand, is a method that takes linear features and combines them, eventually creating polynomial features.
In this project we use a notebook to analyze multivariate regression models using two popular datasets with distinct challenges involved, Iris and Boston Housing. We demonstrate how PolynomialFeatures and PCA techniques can be combined to help regression models achieve very good performances on datasets with irregular distributions.
With PCA simplifying our data, and training a regression model using polynomial features, it is possible to find a good fit without needing a complex model.