Decision Trees §

Group: 4 #group-4

Relations §

• Classification: Decision Trees can be used for Classification tasks
• Machine Learning: Decision Trees are a type of Machine Learning model that makes decisions based on a series of rules or conditions.
• Missing Values: Decision Trees can handle missing values in the data
• Decision Boundaries: Decision Trees create decision boundaries in the feature space
• Numerical Features: Decision Trees can handle both Categorical and Numerical features
• Overfitting: Decision Trees can overfit the training data if not properly pruned
• Regression: Decision Trees can also be used for Regression tasks
• Gini Index: Gini Index is another metric used for selecting the best feature for splitting
• Recursive Partitioning: Decision Trees use a recursive partitioning algorithm to split the data
• Ensemble Methods: Decision Trees can be combined with other models in Ensemble Methods
• Pruning: Pruning is a technique used to prevent overfitting in Decision Trees
• Machine Learning: Decision Trees are a type of Machine Learning model that makes decisions based on a series of rules or conditions, commonly used for classification and regression tasks.
• Entropy: Entropy is used to measure the impurity of a node in Decision Trees
• Supervised Learning: Decision Trees are a Supervised Learning technique
• Information Gain: Information Gain is a metric used to select the best feature for splitting a node
• Feature Importance: Feature Importance can be derived from Decision Trees
• Random Forests: Random Forests are an ensemble method that uses multiple Decision Trees
• Decision Rules: Decision Trees can be represented as a set of Decision Rules
• Categorical Features: Decision Trees can handle both Categorical and Numerical features
• Interpretability: Decision Trees are generally considered interpretable models
• Machine Learning: Decision Trees are a type of Machine Learning algorithm