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This algorithm is also very robust because it uses multiple decision trees to arrive at its result.The random forest algorithm is significantly more accurate than most of the non-linear classifiers.Following are the advantages and disadvantages of the random forest classification algorithm: Advantages

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Random forest combats this issue by using randomness.įYI: Free nlp online course ! Pros and Cons of Random Forest ClassifierĮvery machine learning algorithm has its advantages and disadvantages. That’s because your dataset in decision trees will keep becoming more specific. However, the more you’ll iterate this process the more prone you are to overfitting. You have now used the random forest method to pick a car to buy. You gather all the votes from your friends and decide to buy the car that has the most votes. Now you have several cars to choose from. Suppose you iterate this process and ask ‘n’ friends this question. After his suggestions, you feel dicey about his advice so you ask Aditya about his recommendations and he also asks you about your preferences and other requirements. Here, Supratik is using the Decision Tree method to give you feedback based on your response. He would ask you about your preferences, your budget, and your requirements and would also share his past experiences with his car to give you a recommendation. Suppose you wanted to buy a new car and you ask your best friend Supratik for his recommendations. The following example will help you understand how you use the random forest classifier in your day to day life: Example On the other hand, in a regression problem, you’ll compute the average of all the tree outputs and that would be your end result.Ī random forest Python implementation is much simpler and robust than other non-linear algorithms used for classification problems. In a classification problem, every tree votes and the most popular class is the end result. Every tree relies on an independent random sample. The algorithm generates the individual decision trees through an attribute selection indication. Technically, it is an ensemble algorithm. It’s certainly one of the most sophisticated algorithms as it builds on the functionality of decision trees. Finally, you’ll perform bootstrapping, ie, combine the results of all the decision trees present in your forest. Now, the algorithm will calculate the root node among the k features by picking a node that has the highest information gain.Īfter that, the algorithm splits the node into child nodes and repeats this process “n” times. How does it work?Īssuming your dataset has “m” features, the random forest will randomly choose “k” features where k < m. It forms the basis for the Boruta algorithm, which picks vital features in a dataset. Some of its real-life applications include fraud detection, classification of loan applications, and disease prediction. It has numerous applications in our daily lives such as feature selectors, recommender systems, and image classifiers. After that, they select the best viable solution through votes. These algorithms make decision trees based on a random selection of data samples and get predictions from every tree. On top of that, it uses randomness to enhance its accuracy and combat overfitting, which can be a huge issue for such a sophisticated algorithm. That’s because it consists of multiple decision trees just as a forest has many trees. Why is the random forest classifier called the random forest? It is among the most popular machine learning algorithms due to its high flexibility and ease of implementation. The random forest classifier is a supervised learning algorithm which you can use for regression and classification problems. Random Forest Classifier: An Introduction We’ll cover the advantages and disadvantages of random forest sklearn and much more in the following points. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Enrol for the Machine Learning Course from the World’s top Universities.

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