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Model would become sensitive with highly collinear variables, which means that with small variations in the data, model would return large residual errors in the prediction. Do you wish to watch video for the same?
Which is an example of multiple linear regression?
Multiple Linear Regression. So far, we have seen the concept of simple linear regression where a single predictor variable X was used to model the response variable Y. In many applications, there is more than one factor that influences the response.
Can a linear regression model handle multicollinearity?
So the linear regression model has build = + + , (1) from the above list, as system by itself cannot handle multicollinearity in the variables. There are methods for data scientists to identify highly correlated variables.
Is the effect of having correlated predictors significant?
It is often the case that two (or more) variables will be correlated and both related to the dependent variable. Whether they are significant or not depends on both effect size and cell size.
Challenge is, any machine learning models cannot automatically identify these highly correlated variables and pick only one of these. This is where data scientists are needed to deal with such situations. Let us build linear regression model in python and read the outcome. (‘Coefficients: ’, array ( [ [1., 1., 1.]]))
Clearly says that all three columns are positively & highly correlated and hence it is viable to use only one variable instead of all three as the information that exists in or or is good enough to compute . Challenge is, any machine learning models cannot automatically identify these highly correlated variables and pick only one of these.