Simple and crude understanding and implementation of machine learning integrated learning (a): Introduction to integrated learning algorithms, machine learning two core tasks, integrated learning and boosting Bagging

Integrated learning

learning target

• Understand two core tasks of solving major integrated learning
• You know the principles of integrated bagging
• We know the process of establishing a random decision tree forest
• Why do I need to know random with replacement (Bootstrap) sampling
• Random Forest algorithm to achieve application RandomForestClassifie
• We know the principle of boosting integration
• Know the difference between bagging and boosting the
• Learn gbdt implementation process
  

5.1 Introduction ensemble learning algorithm

1 What is an integrated learning

Integrated learning to solve the problem through the establishment of a single predict several models. It works by generating a plurality of classifiers / model , independently learn and make predictions. Finally, these projections into a combined prediction, and therefore better than any single classifier to make a prediction.

2 Review: machine learning two core tasks

3 Integrated Bagging and boosting learning

As long as a single classifier performance is not too bad, the result of the integration of learning is always better than a single classifier.