What happens when you overfit a regression model?
What happens when you overfit a regression model?
Question 25 options:
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The model captures too much of the random variation in the sample data and fails to predict accurately for the population. |
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The p-value for the F-test of overall significance is greater than 0.05. |
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So many variables are added to the model that the Adjusted {"version":"1.1","math":"<math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>R</mi><mn>2</mn></msup></math>"} value exceeds 1 |
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None of the relationships in the model are found to be statistically significant. |
Overfitting of the model:- When the model performs much better in train data comparison to test data. We said model is overfitting. In simple words, model looks good performing but not in actual.
Reasons of overfitting.
1) Model is too complex: When model have so many independent variable than model will be complex. It will perform good for training data but not for test data.
2) Large training data:- When a model gets trained with so much data, it starts learning from the noise and inaccurate data entries in our data set. So it will give inaccurate predictions for test data but not for train data.
3) Higher variance and low bias in the model
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