Decision Forests

What are Decision Forests? Decision Forests Explained.

Decision forests, also known as random forests, are an ensemble learning method used for both classification and regression tasks. They are composed of multiple individual decision trees, where each tree independently makes a prediction, and the final prediction is determined by combining the predictions of all the trees.

Here are the key characteristics and steps involved in building decision forests:

Ensemble Learning: Decision forests employ ensemble learning, which combines the predictions of multiple models to make more accurate predictions than a single model. Each decision tree in the forest is a weak learner, meaning it may not be highly accurate on its own but contributes to the overall predictive power when combined.

Random Sampling: Decision forests use random sampling with replacement, also known as bootstrapping, to create multiple training datasets. Each tree in the forest is trained on a different bootstrapped dataset, which is formed by randomly selecting instances from the original training data. This sampling process introduces diversity in the individual trees, leading to a more robust and generalized model.

Feature Randomness: In addition to random sampling, decision forests also introduce randomness in feature selection during each tree’s construction. At each split point in a tree, a random subset of features is considered, and the best split is chosen based on these features. This random feature selection further enhances the diversity of the trees and reduces the correlation among them.

Tree Construction: Each decision tree in the forest is constructed using a top-down, recursive process known as recursive partitioning. The tree is built by recursively splitting the data based on different features and their thresholds. The splits are determined using metrics such as Gini impurity or information gain to maximize the separation between classes or reduce the variance in regression tasks.

Prediction Aggregation: Once all the trees are constructed, predictions are made by each tree independently. For classification, the mode (most frequent class) of the individual tree predictions is taken as the final prediction. For regression, the average or median of the individual tree predictions is considered.

Decision forests offer several advantages:

• They are robust against overfitting and tend to generalize well to unseen data due to the ensemble approach and randomness in sampling and feature selection.
• They can handle a large number of input variables, including categorical and numerical features.
• They provide an estimate of feature importance, indicating which features are more informative in making predictions.
• Random forests have become a popular and widely used machine learning algorithm due to their versatility, ease of implementation, and good performance across a wide range of tasks