Random Forests - Building, Using and Evaluating | Fun and Easy Machine Learning | NerdML
Автор: NerdML
Загружено: 2020-08-11
Просмотров: 971
Random Forests make a simple, yet effective, machine learning method. They are made out of decision trees, but don't have the same problems with accuracy. In this video, I walk you through the steps to build, use and evaluate a random forest.
Want to learn why Random Forests are one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning? What this video tutorial explaining the basics of Random Forests.
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Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees.
In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results.
To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm.
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Please find the previous Video link - Decision Tree for Regression Part 3 | NerdML : • Decision Tree for Regression Part 3 | Ne...
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Prerequisites
Basic understanding of Linear Algebra, Probability, Calculus, Matrix & Python programming including pandas, numpy, scikit learn & some visualization tools.
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Creator : Rahul Saini
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Please find the previous Video link -
Decision Tree for Regression Part - 3 | NerdML : • Decision Tree for Regression Part 3 | Ne...
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