Overfitting happens when the model researches the training data to affect the model performance on the latest data significantly. This indicates that we record the disruption in the training data, and we learn the concepts by model. The problem is that concepts that do not employ the testing data negatively affect the ability of the model for classifying the new data; therefore, it decreases the testing data accuracy.
To avoid Overfitting, we have to apply the following methods:
- We collect more data so that we can train the model with diverse samples.
- We can avoid overfitting by using the ensembling methods, like Random Forest. According to the bagging idea, we use them to minimize the change in the projections by joining the result of the multiple decision trees on various samples of the data set.
- By Selecting the correct algorithm, we can avoid overfitting.