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31 Views β’ May 16, 2023 β’ Click to toggle off description
as well.we often do multicollinearity checking using heatmap.
Multicollinearity happens when independent variables in the regression model are highly correlated to each other. It makes it hard to interpret of model and also creates an overfitting problem. It is a common assumption that people test before selecting the variables into the regression model.
Why Multi-Collinearity is a problem?
When independent variables are highly correlated, change in one variable would cause change to another and so the model results fluctuate significantly. The model results will be unstable and vary a lot given a small change in the data or model. This will create the following problems:
It would be hard for you to choose the list of significant variables for the model if the model gives you different results every time.
Coefficient Estimates would not be stable and it would be hard for you to interpret the model. In other words, you cannot tell the scale of changes to the output if one of your predicting factors changes by 1 unit.
#artificialintelligence #machinelearning #deeplearning #dataanalytics #datascience #data #datascienceinterview #shorts #linearregression
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Genre: Education
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Uploaded At May 16, 2023 ^^
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RYD date created : 2023-05-18T09:03:48.097315Z
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