![]() ![]() Random Forest is known to be a robust model for handling over-fitting and optimizing the model’s accuracy. Out-of-Bag Estimates - Monitor Error, Strength & Correlation Therefore, it is advisable to maximize the strength of the trees while minimizing correlation to improve the overall model’s error rate. While increasing the strength of the individual trees decreases the RF error rate. Increasing the correlation between any two trees in the RF increases the overall error rate. The interplay between these two parameters gives the foundation for understanding the working of an RF. Strength is a measure of how accurate the individual classifier is, and Correlation is a measure of dependence between different individual classifiers. The key objective of an ensemble model is to create a classifier that has an error rate better than random guessing.įor the random forest, Breiman introduced two parameters namely strength and correlationon which error rate depends. It choose the feature with the highest Gini index as the split feature in that node.Įach tree acts as a base classifier to make a prediction for the unlabeled data and it aggregates the output from each tree to make the final prediction for the instance. At each node, model tests a subset of features and select based on the Gini Index. Typically, this is equal to log(F), where F is the number of features in the data set. To construct a forest of uncorrelated trees, the algorithm searches for the best feature among the subset of random features. While out-of-bag instances comprises of the remaining examples on which we test the tree’s performance.įurther, during the construction of the individual trees in RF, randomized feature selection is incorporated. In-bag instances includes the sample of examples drawn from the data to construct tress. Through Bootstrap sampling, it constructs each tree using a sample of data drawn randomly from the training data. by combining bootstrap sampling with random features selection to construct a set of trees with controlled variation. Random Forest incorporates randomness in two ways, i.e. It operates by constructing a forest of decision trees and aggregating the output from each tree to give the final prediction. Random forest (RF) is an ensemble learning method developed by Breiman (2001). We also discussed approaches implementing the stated paradigms, namely bagging and boosting. In the previous post, we talked about ensemble learning, its architecture and paradigms. We would look behind the curtains to understand the mechanics on which it works. In this article, we will introduce a state-of-the-art ensemble method - an extension of Bagging - Random Forest. ![]()
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