Training a Machine Learning Model on a Dataset with Highly-Correlated Features

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), we’ve 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 [‘age’, ‘tonnage’, ‘passengers’, ‘length’, ‘cabins’, ‘passenger_density’], if we assume important features have a correlation coefficient of 0.6 or greater with the target variable, then the target variable “crew” correlates strongly with 4 predictor variables: “tonnage”, “passengers”, “length, and “cabins”.

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