In simple words, Random forest builds multiple decision trees (called the forest) and glues them together to get a more accurate and stable prediction. Similarly, in the random forest classifier, the higher the number of trees in the forest, the greater is the accuracy of the results. Generally, the more trees in the forest, the forest looks more robust. As the name suggests, this algorithm randomly creates a forest with several trees. Random forest algorithm is a supervised classification and regression algorithm. That is the only point when Random Forest comes to the rescue. A decision tree model has high variance and low bias, giving us a pretty unbalanced output, unlike the commonly adopted logistic regression, which has high bias and low variance. The algorithm comprises a bundle of decision trees to make a classification, and it is also considered a saving technique when it comes to overfitting a decision tree model. The word ‘Forest’ in the term suggests that it will contain many trees. Random Forest also has a regression algorithm technique. But today, we will be discussing one of the top classifier techniques, which is the most trusted by data experts, and that is Random Forest Classifier. It becomes pretty difficult to intuitively know what to adopt considering the nature of the data. But if we consider the overall scenario, then a maximum of the business problem has a classification task. In the field of data analytics, every algorithm has a price. Finally, for the prediction, a simple majority vote is utilized. On the contrary, for bagging (bootstrap aggregating), subsequent trees do not depend on previous trees, and each one is independently constructed using a bootstrap sample of the data set. Upon completion, a weighted vote is used for prediction. The critical point of the boosting technique is that consecutive trees add extra weight to the points incorrectly predicted by previous predictors. The two best-known ways are boosting and bagging classification trees. The Random Forests classifier belongs to the broader field of ensemble learning (methods that provide and generate several classifiers and aggregate their results). Besides their application to predict the outcome in classification and regression analyses, Random Forest can also be applied to select essential variables and groups and enable a deeper understanding of variable relations. ![]() In the tree-building process, the optimal split for each node is identified from a set of randomly chosen candidate variables. Random forest is a machine learning approach that utilizes many individual decision trees.
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