Diving Deep into Decision Trees and Ensemble Learning: A Summarization of Alexey Grigorev's sessions on the same
In this chapter of the ML Zoomcamp by DataTalks.Club (led by Alexey Grigorev), we dived into Decision Trees and Ensemble Learning —two core components in supervised machine learning that offer high interpretability and flexibility. This chapter addresses decision trees, their structure, splitting methods, as well as ensemble techniques like bagging, boosting, and stacking to improve model performance. Notable briefings on the same are as follows: Decision Trees: Core Concepts and Learning In this section, the course covers decision trees as intuitive, rule-based algorithms that are effective yet prone to overfitting on complex datasets. Key topics include: Splitting Criteria: Decision trees divide data by optimizing splits to minimize classification error. Concepts like "impurity" are introduced, helping learners understand how criteria such as Gini impurity and entropy guide the algorithm in choosing splits that reduce classification mistakes. Overfitting risks are discu