👉 PDT, or Predictive Text Decision Tree, is a machine learning technique used for predictive modeling and decision-making, particularly in natural language processing and text analysis. It constructs a tree-like model of decisions based on input features, where each internal node represents a test on a feature, each branch represents the outcome of the test, and each leaf node represents a class label or prediction. The model learns from labeled data by recursively partitioning the feature space to maximize the homogeneity of the target variable within each leaf node, thereby improving predictive accuracy. This approach is effective for tasks like text classification, sentiment analysis, and information retrieval, where understanding and predicting outcomes based on textual data is crucial.
