Boosting & Stacking Machine Learning Models
Learning about boosting machine learning models!
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Boosting Machine Learning Models
Lesson 1 of 1
- 1In this module we will cover a powerful ensemble method called Boosting. Boosted ensemble methods use weak learners as b…
- 2Adaptive Boosting (or AdaBoost) is a sequential ensembling method that can be used for both classification and regression. It can use any base machine learning model, though it is most commonly…
- 3Let’s take a deeper look at how AdaBoost works! AdaBoost can be used for both regression and classification, but in this example we will be solving a classification problem. We begin with the full …
- 4Wow, that was a lot to take in! Let’s take this opportunity to implement AdaBoost on a real dataset and solve a classification problem. We will be using a dataset from [UCI’s Machine Learning Repo…
- 5Gradient Boosting is a sequential ensembling method that can be used for both classification and regression. It can use any base machine learning model, though it is most commonly used with dec…
- 6Now we will take a deeper look at how Gradient Boosting works. While Gradient Boosting can be applied to any base machine learning model, decision trees are commonly used in practice. In this examp…
- 7Now that we have taken a look at what is going on under the hood, we are ready to implement Gradient Boosting on a real dataset and solve a classification problem. We will be using a dataset from …