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What is a decision tree, and how is it used in data classification tasks?
A decision tree is a flowchart-like structure used for decision-making and classification tasks. It is used in data classification tasks by dividing the dataset into smaller subsets based on feature values.
Describe the components of a decision tree, including internal nodes, branches, and leaf nodes.
Internal Nodes: Decision points for attributes.
Branches: Outcomes of decisions.
Leaf Nodes: Final predictions or classifications.
Explain the steps involved in building a decision tree.
Steps include: selecting the best attribute (using metrics like entropy/information gain), splitting the dataset, and recursively repeating until all nodes are pure or stopping criteria are met.
Define entropy and information gain in the context of decision trees and explain how they influence the construction of the tree.
Entropy: Measure of impurity or disorder.
Information Gain: Reduction in entropy after a dataset split.
These concepts influence the tree construction process by helping to identify the best feature to split on.
What is the Gini index, and how is it used in building decision trees?
A metric to measure node impurity (probability of incorrectly classifying a randomly chosen data point). It identifies best split.
Describe the ID3 algorithm and its role in decision tree construction.
Builds decision trees by recursively selecting attributes that maximize information gain.
How are attributes selected for splitting at each node in a decision tree?
Attributes are selected based on metrics like information gain or Gini index.
Discuss the techniques used to prevent overfitting in decision trees.
Use pruning, limit tree depth, or ensure sufficient data at nodes.
Explain the difference between pre-pruning and post-pruning in decision tree algorithms.
Pre-Pruning: Halts tree growth early based on criteria.
Post-Pruning: Removes branches after the tree is fully grown.
List and explain the advantages and limitations of using decision trees.
Advantages: Simple, interpretable, handles categorical/continuous data.
Limitations: Prone to overfitting, unstable.
Compare and contrast decision trees with Random Forests.
Random forests use multiple decision trees to improve stability and accuracy through bagging.
How do decision trees handle missing values during the training process?
Methods include ignoring missing values, surrogate splits, assigning missing values to most frequent class, imputing before training.
Provide examples of real-world applications where decision trees are effectively used.
Fraud detection, medical diagnosis, and customer segmentation.
How does a decision tree algorithm handle continuous and categorical variables differently?
Continuous variables are split into ranges; categorical variables are split by category.
What methods are used to evaluate the performance of a decision tree?
Use metrics like accuracy, precision, recall, F1 score, or confusion matrix.
