Bagged trees, or a bagging regressor, are common models in data science. In this video, learn how to create and tune a bagging regressor model using the Python library scikit-learn.
- [Instructor] Each machine learning algorithm … has strengths and weaknesses. … A weakness of decision trees is that they're prone … over fitting on the training set. … A way to mitigate this problem, … is to constraint how large a tree can grow. … Bagged trees try to overcome this weakness … by using bootstrapped data, … to grow multiple deep decision trees. … The idea is that matrix protect each other … from individual weaknesses. … What this image shows is that multiple decision trees … come together to make a combined prediction. … In this video, … I'll share with you how you can build a Bagged Tree Model. … The first step is to Import Libraries. … The Dataset used in this notebook … is a housing prices for King County. … The code below loads the dataset. … The goal of this dataset is to predict house prices … based on features like number of bedrooms and bathrooms. … This notebook only selects a small subset … of the features for simplicity. … However, if you have time I encourage you to play …
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- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)