The random forest models are the construction of a multitude of decision trees. In this video, learn how to create a random forest model using the Python library scikit-learn as well as visualize individual trees from random forest models.
- [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 predict house prices …
This course was created by Madecraft. We are pleased to host this content in our library.
- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)