Machine Learning Tutorial 10 - Ensemble Learning in Python Machine Learning
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In this video, we will learn about Ensemble learning in machine learning with python. We can define Ensemble Learning in this way it uses multiple machine learning models or multiple sets of models for the same algorithm which try to make a better prediction. Ensemble Learning model works by training different models on the same dataset and makes prediction individually and once the prediction is made then these results are combines with some statistical methods to get the final prediction
In one sentence we can explain like this there is a dataset where multiple algorithms are trained on the same dataset and then finally predictions are made based on the outcomes of the individual machine learning algorithms.
Bias error is useful to quantify how much on an average are the predicted values different from the actual value. Variance on the other side quantifies how the predictions made on the same observation are different from each other.
Bagging is also known as Bootstrapping. It is a sampling technique in which we create subsets of observations from the original dataset, with replacement. The size of the subsets is the same as the size of the original set
🔊 Watch till last for a detailed description
01:38 What is Ensemble learning?
09:20 Types of Ensemble learning
17:35 Get started with code
27:07 Accuracy comparison
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