Training a Machine Learning Model on a Dataset with Highly-Correlated Features
Last Updated on July 20, 2023 by Editorial Team
Author(s): Benjamin Obi Tayo Ph.D.
Originally published on Towards AI.
1. Import necessary libraries
In the previous article (Feature Selection and Dimensionality Reduction Using Covariance Matrix Plot), weβve shown that a covariance matrix plot can be used for feature selection and dimensionality reduction.
Using the cruise ship dataset cruise_ship_info.csv, we found that out of the 6 predictor features [βageβ, βtonnageβ, βpassengersβ, βlengthβ, βcabinsβ, βpassenger_densityβ], if we assume important features have a correlation coefficient of 0.6 or greater with the target variable, then the target variable βcrewβ correlates strongly with 4 predictor variables: βtonnageβ, βpassengersβ, βlength, and βcabinsβ.
We, therefore, were able to reduce the dimension of our feature space from 6 to 4.
Now, suppose we want… Read the full blog for free on Medium.
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Published via Towards AI