5 Questions
What is the goal of a decision tree?
To segment the predictor space into simple regions
What does pruning in decision trees aim to achieve?
Reduce overfitting by limiting tree depth
What does bagging involve in ensemble learning?
Creating multiple decision trees trained on different bootstrap samples
How are continuous features handled before a split at the root node in a decision tree?
They are turned into categorical variables based on a certain value
What is the purpose of creating ensembles in machine learning?
Aggregating the results of different models to improve predictive performance
Learn about decision trees, their growth process, pruning, and ensemble learning techniques like bagging, random forest, and boosting. Understand how decision trees are used as a flowchart-like structure in supervised learning algorithms to segment predictor space.
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