Why do we prefer a Forest (collection of Trees) rather than a single Tree?

While building a machine learning model, our aim is to generalize the model properly for giving predictions on unseen data.

The problem of overfitting takes place when we have a flexible model. A flexible model is having high variance because the learned parameters like the structure of the decision tree, etc will vary with the training data. On the contrary, an inflexible model is said to have a high bias as it makes assumptions about the training data and an inflexible model may not have the capacity to fit even the training data and in both situations, the model has high variance, and high bias implies the model is not able to generalize new and unseen data points properly.

So, we have to build a model carefully by keeping the bias-variance tradeoff in mind.

The main reason for the overfitting of the decision tree due to not put the limit on the maximum depth of the tree is because it has unlimited flexibility, which means it keeps growing unless, for every single observation, there is one leaf node present.

Moreover, instead of limiting the depth of the tree which results in reduced variance and an increase in bias, we can combine many decision trees that eventually convert into a forest, known as a single ensemble model (known as the random forest ).