Ensemble learning and basket analysis

Chapters 7 and 8 of Practical Machine Learning System were close to what I wanted to do, so I summarized them quickly. (No formulas and codes, just an overview) I didn't write the association rule mining because there was a beautifully organized material. (Reference at the bottom)

Ensemble learning

Ensemble learning is the process of fusing multiple individually learned learners to improve generalization ability (prediction ability for unlearned data) and create one learner.

As a feature, each learner can be considered as a new feature quantity, and a new combination method is learned based on the training data.

As the saying goes, ** 3 people are the wisdom of Manjushri **. Discrimination ability improves by increasing the number of learners. Ensemble learning has the advantages of simplicity (just prepare multiple learning devices) and versatility (applicable to any learning device) in addition to high discrimination ability.

For that purpose, it is necessary to adjust the weight of each learner. (It is rare that the weights of all learners are uniform). That is, the value obtained by adding all the products of the ratings predicted by each learner and the determined weights is adopted as the final predicted rating. (Weighted average) The optimal ** weight ** is learned from the data.

Figure (like a multi-learner version of a neural network)

It was also included in Python's Scikit-Learn. 1.9. Ensemble methods — scikit-learn 0.15.2 documentation

Typical ensemble learning algorithm

Bagging, Bootstrap Aggregating

How it works

• Make many hypotheses by sampling the example multiple times
• Create a final hypothesis with a collection of them

Feature

• Stabilization of unstable learning models (neural networks, etc.)

Feature

• Collect a lot of less powerful machine learning to create predictors that are inherently more capable than individual learners.

[Random Forest](http://ibisforest.org/index.php?%E3%83%A9%E3%83%B3%E3%83%80%E3%83%A0%E3%83% 95% E3% 82% A9% E3% 83% AC% E3% 82% B9% E3% 83% 88)

How it works

• Bootstrap sample that generates group B subsamples by random sampling from the observation data that you want to train
• Create a $ B $ decision tree using each subsample as training data
• Create nodes by the following method until the specified number of nodes $ n_ {\ mathit {min}} $ is reached.
• Randomly select $ m $ of the explanatory variables of the training data
• Determine the node split function using the selected explanatory variables that best classify the training data and the thresholds at that time.

Feature

• Works well with many explanatory variables
• Fast learning and evaluation
• Decision tree learning is completely independent and can be processed in parallel
• The importance (contribution) of explanatory variables can be calculated
• Evaluation like cross-validation is possible by calculating Out of Bag error (data that is not used for learning on average about 1/3 of the training data)
• Compared to AdaBoost, it is less dependent on a specific explanatory variable, so it gives output that can be missing even if the explanatory variable of query data is missing.

[Boosting](http://ibisforest.org/index.php?%E3%83%96%E3%83%BC%E3%82%B9%E3%83%86%E3%82%A3 % E3% 83% B3% E3% 82% B0)

How it works

• Learning by changing the weight of the example sequentially
• Combine to improve accuracy

Method

• AdaBoost sequentially updates the weights during resampling of training data (understanding the differences that occur in the data between samples by repeating the extraction of data at random), resulting in machine learning. Finally, add weights to machine learning and add them together to create an integrated learner.
• Learn many times with weight without throwing away cases
• Learn with increased weight for weak cases
• Learn T hypotheses and take a majority vote
• The effect of AdaBoost is remarkable for data without noise

Basket analysis

Another analytical method for learning recommender systems. The data handled by the basket analysis is only about which items were purchased together, and does not require information such as whether or not you like the item. (Item-based pockets in collaborative filtering)

This basket analysis is not just applicable to "shopping carts". It can be grouped together and can be applied to any target if you need to recommend the items in it. For example, recommending a recommended web page to the user from the browsing history of the browser.

Famous story of beer and diapers in basket analysis

reference

Practical machine learning system Ensemble learning A story about studying ensemble learning (miscellaneous notes?) Implementation of AdaBoost by Splus [Data science by R] Group learning Random forest [What is out of bag error in Random Forests?] (http://stackoverflow.com/questions/18541923/what-is-out-of-bag-error-in-random-forests) 2nd How to find out what products sell well with a certain product-The idea of market basket analysis 6. Product analysis method (ABC analysis, association analysis) 2nd: Association Analysis ~ "Statistics You Want to Use" Series ~ Association analysis