AI News, Ensemble Learning to Improve Machine Learning Results

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.

Bagging and Boosting

Ensemble Methods and Random Forests

Introduction to Ensembles

Boosting In Code - Georgia Tech - Machine Learning

Watch on Udacity: Check out the full Advanced Operating Systems course for free ..

Creating a Decision Tree with IBM SPSS Modeler

This clip demonstrates the use of IBM SPSS Modeler and how to create a decision tree. Such a tool can be a useful business practice and is used in predictive ...

More Data Mining with Weka (5.4: Meta-learners for performance optimization)

More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 4: Meta-learners for performance optimization ...

Advanced Data Mining with Weka (2.4: MOA classifiers and streams)

Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: MOA classifiers and streams ..

MWMOTE for Imbalanced Data Set Learning and DSUS for Imbalance Classification Problems

Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes ...

1. Why it Helps to Combine Models

Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning:

Ensemble learning

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any ...