Underfitting in Machine Learning
Underfitting refers to a model that can neither model the training data nor generalize to new data.
It is the situation of- High Bias and High Variance
An underfit machine learning model is not a suitable model and will be obvious as it will have poor performance on the training data.
Underfitting is often not discussed as it is easy to detect given a good performance metric. The remedy is to move on and try alternate machine learning algorithms.
Techniques to reduce underfitting :
- Increase model complexity
- Increase number of features, performing feature engineering
- Remove noise from the data.
- Increase the number of epochs or increase the duration of training to get better results.