In linear regression, regularization is a process of making the model more regular or simpler by shrinking the model coefficient to be closer to zero or absolute, ultimately to address over fitting.
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Updated
Nov 24, 2021
In linear regression, regularization is a process of making the model more regular or simpler by shrinking the model coefficient to be closer to zero or absolute, ultimately to address over fitting.
Comparing Ridge and LASSO model to find the best accuracy for Home Price Prediction
Linear Regression on Medical Insurance Dataset
Project to predict production quantities for a given dataset using Machine Learning algorithms.
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