Overfitting

Overfitting is when the algorithm focusses on capturing minor details in the training data, potentially at the expense of generalizability to unseen data. The ideal scenario should capture the underlying patterns in the data that generalizes well to unseen examples.

How to avoid overfitting?

Pruning: Setting a maximum depth for the tree. This restricts the tree from growth and prevents it from becoming overly complex.

Use more training data: More diverse and representative data, allows the model to learn the underlying patterns more effectively rather than also learning the noise or specific anomalies in a smaller dataset

Use cross-validation to tune model complexity: Dividing the data into multiple training and validation sets repeatedly trains the model on different subsets of the data and evaluating it on the remaining parts, providing a comprehensive measure of the model’s performance.

Random Forest:

• This ensemble method involves multiple decision trees each trained on a random subset of features and data points. The introduction of randomness during the training process makes the model less prone to overfitting.
• By averaging the predictions from the individual trees, you can reduce the overall variance and improve generalizability.