Day 10 - Overfitting in Predictive Models

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        By Jerin Lalichan 


Overfitting

    It is a modeling error that occurs when the model aligns very closely to the data points which used to train the model. As a result, the model fails to perform well with other data points. It becomes useful for the training g data set only. This happens when the model is trained for too long or if the model is complex.

( In Regression)


(In Classification)


    Low bias and High variance are indicators of overfitting. In order to detect the presence of overfitting, a part of the training dataset is set aside as a test set to check for overfitting. The training accuracy will be higher than that of testing accuracy.

How to avoid Overfitting

Below are a number of techniques that you can use to prevent overfitting:
  1. Early Stopping
  2. Train with more data
  3. Data augmentation
  4. Feature selection
  5. Regularization
  6. Ensemble methods
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  I am doing a challenge - #66DaysofData  in which I will be learning something new from the Data Science field for 66 days, and I will be posting daily topics on my LinkedIn, On my GitHub repository, and on my blog as well.


Stay Curious!  





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