Machine learning in trading: theory, models, practice and algo-trading - page 3339
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statistical learning
kozul is self-promotion, a new sticker on old trousers.
Where is the statistical output after resampling and cv? And construction of the final classifier. Take this topic and develop it. This is the basis of kozul.
Kozul is unfair advertising, a new sticker on old trousers.
Tuls for creatin' efective modells, comparing multiple modells vis a vis resampling. Next should be something like stat inference and unbiased model building.
This is the standard of machine learning and much of the book deals with these very issues, which are many years old and for which many tools have been invented. Part 3 of the book is called: Tools for Creating Effective Models with the following content:
- 10 Resampling for performance evaluation
- 11 Comparing models with resampling
- 12 Model tuning and the dangers of overfitting
- 13 Grid search
- 14 Iterative search
- 15 Viewing multiple models
In addition there is chapter 20"Ensembles of Models" which tells how to build the final model.
We need statistical lerning.
Need? Please: CRAN Task View: Machine Learning & Statistical Learning
This is for beginners tips, you need a kozul and the ability to think
A model ensemble, where the predictions of multiple single learners are aggregated to make one prediction, can produce a high-performance final model. The most popular methods for creating ensemble models are bagging(Breiman 1996a), random forest(Ho 1995;Breiman 2001a), and boosting(Freund and Schapire 1997). Each of these methods combines the predictions from multiple versions of the same type of model (e.g., classifications trees). However, one of the earliest methods for creating ensembles ismodel stacking(Wolpert 1992;Breiman 1996b).
Model stacking combines the predictions for multiple models of any type. For example, a logistic regression, classification tree, and support vector machine can be included in a stacking ensemble.
This chapter shows how to stack predictive models using thestacks package . We'll re-use the results from Chapter15 where multiple models were evaluated to predict the compressive strength of concrete mixtures.
The process of building a stacked ensemble is:
20.5 CHAPTER SUMMARY
This chapter demonstrated how to combine different models into an ensemble for better predictive performance. The process of creating the ensemble can automatically eliminate candidate models to find a small subset that improves performance. Thestacks package has a fluent interface for combining resampling and tuning results into a meta-model.
This is the author's view of the problem, but it is not the only way to combine multiple models - there are stacks packages in R for combining models. For example, caretEnsemble: Ensembles of Caret Models
everything will be slow, cotton.
It also seems that the book confuses ensemble and stacking. In short, this is a normal approach, but it can be cotton in production.
As you recently gave a link to Vladimir's article. An example of the most wacky TC creation.
What kind of cottoniness?
What's with the cottoniness?
I suggest we go back to kozul, statistical learning and reliable AI.
P.Z.
Figure out the finer details of it.
I suggest we go back to kozul, statistical learning and reliable AI.
P.Z.
Figure out the finer details of it.
If you're straight to the details, here's a read on it