Decision Tree
Gradient Boosting Decision Tree
In the previous article, we’ve talked about AdaBoost which combines output of weak learners into a weighted sum that represents the final output of the boosted classifier. If you know little about AdaBoost or additive model, we highly recommend you read the article first. Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.
Random Forest
Random forests are an ensemble learning method for classification, regression, and other tasks. They operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or the mean prediction (regression) of the individual trees. Each tree is grown on a random subset of the training data and considers only a random subset of the features at each split, so the forest as a whole corrects for a decision tree’s habit of overfitting its training set: when it is time to make a prediction, the trees vote and the majority wins.
Decision Tree
What’s a decision tree? A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute (e.g. whether Income is below 50K), each branch represents an outcome of that test, and each leaf node holds a prediction: a class label for classification, or a value for regression. A path from the root to a leaf is therefore a chain of conditions that ends in a decision.