Question

You've built a random forest model with 10000 trees. You got delighted after getting training error as 0.00. But, the validation error is 34.23. What is going on? Haven't you trained your model perfectly?

Answer 1

The model is likely overfitting the training data, resulting in poor performance on unseen data.

Step-by-step explanation:

In this case, the model seems to be overfitting the training data. Overfitting occurs when a model is too complex and learns the noise or random fluctuations in the training data, resulting in poor performance on unseen data.

A training error of 0.00 suggests that the model is able to perfectly fit the training data, but it might not generalize well to new data. The validation error of 34.23 indicates that the model performs poorly on new, unseen data.

To address this issue, you can try reducing the complexity of the model, such as by limiting the number of trees in the random forest or using feature selection techniques to choose only the most important features.