Model fitting is a measure of how well a machine learning model generalizes to similar data to that on which it was trained. A model that is well-fitted produces more accurate outcomes. A model that is overfitted matches the data too closely. A model that is underfitted doesn’t match closely enough.
Overfitting
When a massive amount of data trains a machine learning model, it tends to learn from the noise and inaccurate data entries. Here the model fails to characterize the data correctly.
Underfitting
It is the scenario when the model fails to decipher the underlying trend in the input data. It destroys the accuracy of the machine learning model. In simple terms, the model or the algorithm does not fit the data well enough.