Decision Tree
A decision tree is a supervised learning algorithm that makes predictions by learning a series of if-then rules from training data. Decision trees are interpretable and form the basis of powerful ensemble methods like Random Forest.
Understanding Decision Tree
A decision tree is a supervised learning algorithm that makes predictions by recursively splitting data based on feature values, forming a tree-like structure of if-then rules. Each internal node represents a test on a feature, each branch represents an outcome, and each leaf node holds a prediction. Decision trees are prized for their interpretability and are widely used in healthcare diagnostics, credit scoring, and customer segmentation. However, individual trees are prone to overfitting, which is why ensemble learning methods like random forests and gradient boosting combine many trees to improve accuracy and robustness. Feature importance scores from decision trees also support feature engineering and explainable AI efforts. Pruning strategies and hyperparameter tuning on tree depth help manage model complexity and improve generalization.
Category
Machine Learning
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Machine Learning Terms
Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.