Describe Overfitting: Overfitting is one of the major problems that is observed while training machine learning models. It usually means the model we built is not able to generalize well on the unseen data, which results in a higher error rate. To put it in other words, the model was trained well on training data and yielded less error on the training set. However, when we try to test the model on unseen data points the model did not perform well as it is unable to generalize well. To avoid overfitting, there are couple of techniques: 1. Include more data points 2. Use cross-validation techniques 3. Use regularization techniques