overfitting
An overfitting model is the one that fits well with the training data, i.e. little or no error, however it does not generalized well to the unseen data
a model matches the training data almost perfectly, but does poorly in validation and other new data
overfitting capturing spurious (false) patterns that won’t recur in the future, leading to less accurate predictions
How to avoid overfitting?
- try out another algorithm that could generate a simpler model from the training data set
- adds a regularization term to the algorithm, i.e. penalizing the model that is over-complicated so that the algorithm is steered to generate a less complicated model while fitting the data