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 “Overfitting”, your model is not generalized for unknown data set.
Here, I need your help for methods to avoid “Overfitting” and what are the metrics to validate it.