Decision Trees in Machine Learning
Decision trees are hierarchical structures built using nodes and edges. Each internal node represents a feature or attribute, branches emanate from these nodes, and leaf nodes represent the outcomes or decisions. These trees facilitate data analysis by sequentially partitioning the data based on the selected features until a final decision is reached. Each branch corresponds to a range or category, guiding the decision-making process effectively.
How do Decision Trees Work?
The working principle of decision trees revolves around finding the most optimal splits in the dataset. The splitting criterion depends on various algorithms such as Gini Index, Information Gain, or Chi-Square Test. The tree-building algorithm recursively selects features and thresholds to create binary splits, aiming to maximize the information gained or minimize impurity at each node. This process continues until the desired depth or purity level is attained.
