Machine Learning MCQ - How to prevent overfitting in decision trees

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Machine Learning MCQ - Prevent or reduce overfitting in decision trees - How?

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1. Select all strategies below that can help prevent or reduce overfitting in decision trees

a) Not restricting the depth of the decision tree

b) Pruning the decision tree based on a validation set accuracy

c) Use more features to represent each example

d) None of the above

 

Answer: (b) Pruning the decision tree based on a validation set accuracy   Decision tree pruning is one of many techniques used to prevent the tree from overfitting.   Pruning is a technique that removes parts of the decision tree and prevents it from growing to its full depth and complex. Pruning removes those parts of the decision tree that do not have the power to classify instances. A validation set is a subset of data (or training data) used to evaluate and improve a model's performance during training. We say that a machine learning model overfits when it shows low training error and high true error. Overfitting occurs when a model fits too closely to the training data and may become less accurate when encountering new data or predicting future outcomes. If the training error is much lower than the validation error, it means that the model is overfitting the training data.   Why not option (a)? Letting a tree to grow beyond a depth might lead to overfit. To limit the growth of a decision tree, maximum depth can be set. Maximum depth a decision tree is allowed to grow is a type of pruning techniques (pre-pruning).   Why not option (c)? Selection of the most relevant and informative features to use in the Decision Tree is very much necessary rather than using more features.   How to avoid overfitting in decision trees? We can use one or more of the following to overcome overfitting in decision trees; Pruning Regularization Early stopping Hyperparameter tuning Ensemble methods Minimum samples per leaf node Cross-validation Training with more data Data augmentation

 

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What is pruning in decision trees in machine learning?

Common problem in decision tree is overfitting

What is validation set and how does its accuracy helps in pruning the decision trees?

Overfit means low training error and high test (true) error. It is considered as the failure of the model to generalize

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