Question: 1. If we include predictors in a multiple linear regression model that are highly correlated, which of the following do we need to be concerned

1. If we include predictors in a multiple linear regression model that are highly correlated, which of the following do we need to be concerned with?

Overfitting

Curse of dimensionality

Outliers

Multicollinearity

2. You need to determine which predictors to include in your model. You have already used your domain knowledge, and you would now like to use a feature selection algorithm. Which of the following is NOT a feature selection algorithm?

Regularization

Exhaustive search

Multiple Linear Regression

Backward Elimination

In MLR, estimated beta coefficients are those that _______________.

maximize error in the validation set

maximize error in the training set

minimize error in the validation set

minimize error in the training set

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