Before diving into ensemble models, let's discuss the learning curve. A learning curve is a plot of training and validation set error over the number of training steps. This plot helps us understand how training and validation errors change as training progresses.
We will divide it into three regions, refer to Diagram 1.

• Region A: Underfitting Region

  This is the initial phase of training. Since training has just started, both training and validation errors are high. However, an important observation is that these errors are close to each other. This scenario is referred to as high bias (due to poor training) and low variance (since the errors are similar). This situation is known as underfitting.

• Region C: Overfitting Region

  After several training steps, the algorithm begins to fit the noise in the training dataset. As a result, its generalization ability declines, leading to high errors on the validation set. This situation is called overfitting. It is characterized by low bias (low training error) and high variance (a large difference between training and validation errors).

• Region B: Optimal Fit

  This region represents an ideal balance, low training error and low validation error. Here, the training algorithm fits the data well while maintaining strong generalization capabilities.

Diagram 1: Learning Curve

Now, let's understand the challenges faced by decision tree models and how to overcome them.

Overfitting Problem in Decision Tree Models

Decision trees are prone to overfitting, meaning they may attempt to make correct predictions even for noisy data, thereby losing generalization capability.
Why does this happen?
Each leaf node in a decision tree represents a data partition. More leaf nodes mean more partitions, and the decision tree makes separate predictions for each partition. As the number of partitions increases, the decision tree starts overfitting the training data, leading to high errors on the validation data.

How to Prevent Overfitting in Decision Trees

Tree depth is a crucial hyperparameter in decision trees. Proper hyperparameter tuning can help mitigate overfitting. You can find a detailed guide on hyperparameter tuning here.

Another approach to reducing overfitting in decision trees is to use ensemble models. Various techniques exist within ensemble learning, one of which is bagging. Random Forest is an ensemble method similar to bagging that effectively addresses the overfitting problem in decision tree algorithms.