The steps that are included while performing the random forest algorithm are as follows:
Step-1: Pick K random records from the dataset having a total of N records.
Step-2: Build and train a decision tree model on these K records.
Step-3: Choose the number of trees you want in your algorithm and repeat steps 1 and 2.
Step-4: In the case of a regression problem, for an unseen data point, each tree in the forest predicts a value for output. The final value can be calculated by taking the mean or average of all the values predicted by all the trees in the forest.
and, in the case of a classification problem, each tree in the forest predicts the class to which the new data point belongs. Finally, the new data point is assigned to the class that has the maximum votes among them i.e, wins the majority vote.