From the course: Machine Learning with Scikit-Learn

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Random forests using scikit-learn

Random forests using scikit-learn - scikit-learn Tutorial

From the course: Machine Learning with Scikit-Learn

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Random forests using scikit-learn

- [Instructor] Each machine learning algorithm has strengths and weaknesses. Bagged tree models use many trees to protect individual decision trees from overfitting. However, bagged tree models are not without weaknesses. Suppose you have one very strong feature in a data set, most of the trees will use that feature as the top split. This will result in many similar trees. You can think of random forest as a variant of a bagged tree model. The difference is that each time a split's considered, only a portion of the total number of features are split candidates. In short, random forests make the individual decision trees less correlated In this video, I'll share with you how you can build a random forest model using Scikit-Learn. The first step is to import libraries. The next step is to load a dataset. This dataset contains house sale prices for King County. The code below loads the dataset. The goal of this dataset is to…

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