Random Forest
The decision tree algorithm that you saw earlier faced the problem of overfitting. Since you fit only one tree on the training data, there is a high chance that the tree will overfit the data without proper pruning. For example, referring to the Amazon sales case study that we discussed at the start of this chapter, if your model learns to focus on the inherent randomness in the data, it will try to use that as a baseline for future predictions. Consider a scenario where out of 100 customers, 90 bought a beard wash, primarily because most of them were males with a beard.
However, your model started thinking that this is not related to gender, so the next time someone logs in during the sale, it will start recommending beard wash, even if that person might be female. Unfortunately, these things are very common but can really harm the business. This is why it is important to treat the overfitting of models. The random forest algorithm reduces variance/overfitting by averaging...
