What is Tree-based Algorithms?
Tree-based algorithms are a class of supervised machine learning models that construct decision trees to typically partition the feature space into regions, enabling a hierarchical representation of complex relationships between input variables and output labels. Examples of notable are random forests, Gradient Boosting techniques and decision trees, using recursive binary split based on criteria like Gini impurity or information gain etc. These algorithms show versatility in use in classification and regression functions, for robustness against overfitting by ensemble methods and generates more individual trees Ability to allow exploratory analysis of feature importance, which is economical, which contributes to widespread application in various fields like healthcare and natural language processing.
Tree Based Machine Learning Algorithms
Tree-based algorithms are a fundamental component of machine learning, offering intuitive decision-making processes akin to human reasoning. These algorithms construct decision trees, where each branch represents a decision based on features, ultimately leading to a prediction or classification. By recursively partitioning the feature space, tree-based algorithms provide transparent and interpretable models, making them widely utilized in various applications. In this article, we going to learn the fundamentals of tree based algorithms.
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