![]() This score will help to choose the most important features and drop the least important ones for model building. Then it scales the relevance down so that the sum of all scores is 1. It automatically computes the relevance score of each feature in the training phase. ![]() Scikit-learn provides an extra variable with the random forest model, which shows the relative importance or contribution of each feature in the prediction. The random forest also offers a good feature selection indicator. Select the prediction result with the most votes as the final prediction.Perform a vote for each predicted result.Construct a decision tree for each sample and get a prediction result from each decision tree.Select random samples from a given dataset.Originally published at How does Random Forest work? It is more simple and powerful compared to the other non-linear classification algorithms. ![]() In the case of regression, the average of all the tree outputs is considered as the final result. In a classification problem, each tree votes, and the most popular class is chosen as the final result. Each tree depends upon the independent random sample. Every individual decision trees are generated using an attribute selection indicator such as information gain, gain ratio, and Gini index of each attribute. This collection of decision tree classifiers is known as the forest. ![]() Technically, the random forest is an ensemble method (based on the divide-and-conquer approach) of decision trees generated on the randomly split dataset. This whole process (first and second part both) of recommendation from friends and voting for finding the best place is known as the Random forest algorithm. Voting means choosing the best place for given recommendations on the basis of friends’ experience. Second, after collecting all the recommendations and you performed the voting procedure for selecting the best place. Here each friend makes a selection of the places he or she has visited so far. This part is using the decision tree algorithm. First, asking friends about their individual travel experience and getting one recommendation out of multiple places they have visited. In the above decision process, there are two parts. The place with the highest number of votes will be your final choice for the trip. Then, you ask them to vote(or select one best place for the trip) from a given list of recommended places. Now you have to make a list of those recommended places. You will get some recommendations from every friend. Let’s suppose you have decided to ask your friends and talked with them about their past travel experience in various places. So what you do to identify a better place which you like? You can search online read lots of people’s opinions on travel blogs, Quora, travel portals, or you can also ask your friends. Suppose you want to go on a trip and you would like to go to a place which you will like. Let’s understand the random forest in layman’s words. Photo by Sarah Evans on Unsplash Random Forest Algorithm
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