From the course: Machine Learning and AI Foundations: Value Estimations
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Train the value estimator
From the course: Machine Learning and AI Foundations: Value Estimations
Train the value estimator
- [Instructor] Open up train_model part 3.py. Let's create and train our machine learning model. We're going to use scikit-learn's gradient boosting regressor. Creating the gradient boosting regressor model is just one line of code. It's called gradient boosting regressor because we're using the gradient boosting algorithm and it's building a model for value prediction. Another name for value prediction is regression. We also need to set the hyper-parameters that control how the gradient boosting regressor model will run. There are a lot of hyper-parameters for this model. We'll start by using reasonable guesses for each but later we'll learn how to tune these parameters to get the best result. First, n_estimators tells the model how many decision trees to build. Higher numbers usually allow the model to be more accurate but it increases the amount of time required to run the model. We'll start with 1000. Learning rate controls how much each additional decision tree influences the…
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