Standardize both the predictor and response variables.One way to get around this problem is to use a method known as partial least squares, which works as follows: When this occurs, a model may be able to fit a training dataset well but it may perform poorly on a new dataset it has never seen because it overfits the training set. This occurs when two or more predictor variables in a dataset are highly correlated. One of the most common problems that you’ll encounter in machine learning is multicollinearity.
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