7/28/2023 0 Comments Bagging random forest![]() Bootstrap AggregationĪs was mentioned in the article on decision tree theory one of the main drawbacks of DTs is that they suffer from being high-variance estimators. Two of the following ensemble techniques–bagging and random forests–make heavy use of bootstrapping techniques, and they will now be discussed. ![]() These are then used to allow "meta-learner" or "ensemble" methods to reduce the variance of their predictions, thus greatly improving their predictive performance. The idea is to repeatedly sample data with replacement from the original training set in order to produce multiple separate training sets. In quantitative finance applications it is often impossible to generate more data in the case of financial asset pricing series as there is only one "history" to sample from. It is often used as a means of quantifying the uncertainty associated with a machine learning model.įor quantitative finance purposes bootstrapping is extremely useful since it allows generation of new samples from a population without having to go and collect additional "training data". The Bootstrapīootstrapping is a statistical resampling technique that involves random sampling of a dataset with replacement. If you lack familiarity with decision trees it is worth reading the introductory article first before delving into ensemble methods.īefore discussing the ensemble techniques of bootstrap aggegration, random forests and boosting it is necessary to outline a technique from frequentist statistics known as the bootstrap, which enables these techniques to work. In subsequent articles it will be shown how to apply such ensemble methods in real trading strategies using the QSTrader framework. Once the theory of these ensemble methods has been discussed they will all be implemented in Python using the Scikit-Learn library on financial data. However, DTs provide a "natural" setting to discuss ensemble methods and they are often commonly associated together. These statistical ensemble techniques are not limited to DTs, but are in fact applicable to many regression and classification machine learning models. ![]() In this article it will be shown how combining multiple DTs in a statistical ensemble will vastly improve the predictive performance on the combined model. In the article it was mentioned that the real power of DTs lies in their ability to perform extremely well as predictors when utilised in a statistical ensemble. In a previous article the decision tree (DT) was introduced as a supervised learning method.
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