Overfitting on a model

[swarup17]

Correct me if I am wrong, Overfit is the issue where the train model learns by heart without learning the patterns which affects the test to generalize the data set. If it is so then our train model should not work on the test which implies its a overfit so no need to test further whether its overfit or not. We should straightly go for solving the overfit issue.

[AishwaryaSingh]

Hi,

That is correct. Overfitting is when your model performs extremely well on the train dataset but not on the test dataset. The following discussion threads might help you get more clarity -

What is meaning of overfitting and underfitting?

Can anyone plz help me understand the concept of overfitting and underfitting in layman’s term? i read many links in google still not able to understand it properly. @Lesaffrea @kunal @shuvayan @Sunil

How to avoid overfitting in Prediction?

I am recently looking at the methods to improve the power of model and there are various methods people have suggested: Focus on hypothesis generation Data Cleaning and Exploration (Find the relation between features) Right selection of method (machine learning technique) Cross-Validation of model While reading all these methods, I came across terms Overfitting and Underfitting. My understanding about “Underfitting” is, you have not predicted well or power of prediction is low and for “Overfi…

[mdbleachit]

What you really need to know is “bias variance tradeoff”.