What is bagging and boosting. What are different types of learning models. Explain Tree based models.

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Accepted Answer

Bagging and boosting are ensemble learning techniques. Tree based models are decision trees used for classification and regression.

Bagging (Bootstrap Aggregating) involves training multiple models on different subsets of the training data and combining their predictions.
Boosting involves training multiple models sequentially, with each model correcting the errors of its predecessor.
Different types of learning models include decision trees, random forests, gradient boosting machines, support vector machines, neural networks, etc.
Tree based models are decision trees or ensemble methods based on decision trees, such as random forests and gradient boosting machines.
Decision trees are hierarchical structures where each internal node represents a decision based on a feature, and each leaf node represents the outcome.
Random forests are an ensemble of decision trees, where each tree is trained on a random subset of the training data and features.
Gradient boosting machines build trees sequentially, with each tree fitting the residual errors of the previous tree.
Support vector machines are used for classification and regression tasks by finding the hyperplane that best separates the classes in the feature space.
Neural networks are a set of algorithms modeled after the human brain, consisting of layers of interconnected nodes that process information.

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