Situation of Underfitting, How to overcome from the situation of underfitting?

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 :

  1. Increase model complexity
  2. Increase number of features, performing feature engineering
  3. Remove noise from the data.
  4. Increase the number of epochs or increase the duration of training to get better results.