A random forest is a machine learning technique that’s used to solve regression and classification problems. It utilizes ensemble learning, which is a technique that combines many classifiers to provide solutions to complex problems. A random forest algorithm consists of many decision trees.
Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model.
As the name suggests, “Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset.” Instead of relying on one decision tree, the random forest takes the prediction from each tree and based on the majority votes of predictions, and it predicts the final output. The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting.
Below are some points that explain why we should use the Random Forest algorithm:
- It takes less training time as compared to other algorithms.
- It predicts output with high accuracy, even for a large dataset it runs efficiently.
- It can also maintain accuracy when a large proportion of data is missing.
Random Forest works in two-phase first is to create the random forest by combining N decision trees, and the second is to make predictions for each tree created in the first phase.