ID3algoritmi

ID3 algoritmi is a popular algorithm used in machine learning for constructing decision trees. Developed by Ross Quinlan, it stands for "Iterative Dichotomiser 3". The primary goal of ID3 is to create a model that predicts a target variable based on a set of input features. It operates by recursively partitioning the dataset based on the feature that provides the most information gain at each step.

The core idea behind ID3 is to select the attribute that best separates the data into distinct classes. Information gain is calculated using the concept of entropy, which measures the impurity or disorder in a set of data. An attribute with high information gain is considered more effective in classifying the data. The algorithm starts with the entire dataset and iteratively splits it into subsets based on the values of the chosen attribute. This process continues until all subsets are pure, meaning they contain instances of only one class, or until all attributes have been used. ID3 is a greedy algorithm, meaning it makes the locally optimal choice at each step, which may not necessarily lead to the globally optimal decision tree. While influential, ID3 has limitations, such as its inability to handle continuous attributes and its susceptibility to overfitting.