AI News, Ensemble Learning to Improve Machine Learning Results
- On Sunday, June 3, 2018
- By Read More
Ensemble Learning to Improve Machine Learning Results
The decision tree shows the axes’ parallel boundaries, while the k=1 nearest neighbors fit closely to the data points.
The bagging ensembles were trained using 10 base estimators with 0.8 subsampling of training data and 0.8 subsampling of features.
As a result, the bias of the forest increases slightly, but due to the averaging of less correlated trees, its variance decreases, resulting in an overall better model.
Instead of looking for the most discriminative threshold, thresholds are drawn at random for each candidate feature and the best of these randomly-generated thresholds is picked as the splitting rule.
The main principle of boosting is to fit a sequence of weak learners− models that are only slightly better than random guessing, such as small decision trees− to weighted versions of the data.
In subsequent boosting rounds, the weighting coefficients are increased for data points that are misclassified and decreased for data points that are correctly classified.
- On Wednesday, June 26, 2019
Bagging and Boosting
Ensemble Methods and Random Forests
Introduction to Ensembles
Boosting In Code - Georgia Tech - Machine Learning
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1. Why it Helps to Combine Models
Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning:
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any ...