So, this dataset is given to the Random forest classifier. The working of the algorithm can be better understood by the below example:Įxample: Suppose there is a dataset that contains multiple fruit images. Step-5: For new data points, find the predictions of each decision tree, and assign the new data points to the category that wins the majority votes. Step-3: Choose the number N for decision trees that you want to build. Step-2: Build the decision trees associated with the selected data points (Subsets). Step-1: Select random K data points from the training set. The Working process can be explained in the below steps and diagram: Random Forest works in two-phase first is to create the random forest by combining N decision tree, and second is to make predictions for each tree created in the first phase. It can also maintain accuracy when a large proportion of data is missing.It predicts output with high accuracy, even for the large dataset it runs efficiently.It takes less training time as compared to other algorithms.The predictions from each tree must have very low correlations.īelow are some points that explain why we should use the Random Forest algorithm:.There should be some actual values in the feature variable of the dataset so that the classifier can predict accurate results rather than a guessed result.Therefore, below are two assumptions for a better Random forest classifier: But together, all the trees predict the correct output. Since the random forest combines multiple trees to predict the class of the dataset, it is possible that some decision trees may predict the correct output, while others may not. The below diagram explains the working of the Random Forest algorithm: Note: To better understand the Random Forest Algorithm, you should have knowledge of the Decision Tree Algorithm. The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting. 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.Īs 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. It can be used for both Classification and Regression problems in ML. Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique.
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