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Random Forest

Random Forest, as the name suggest, it’s the bagging of many Decision Trees, in this way we get better results than using just a single tree.

Random Forest not only sample subsets of a given data set but also sample subsets or features, letting each model train on a different set of features, as a consequence of this, it is possible to use it as a way to measuring features importance, for example, it can be used to sort the features by their importance and then use the top percentile of the feature to train another Random Forest model. Such a pipeline of models and data pre-processing engineering and inference, and often used in ML competitions.

This concept can be very helpful in future analysis so keep it on mind.

Regression Analysis

In the context of Algorithmic Trading we’ll be more concerned about regression, rather than classification, which basically means, prediction of future values (use a given data set of prices, to forecast future prices)

Regression is a common well established concept in statistics.

The goal of Regression is to find a line or a hyperline that best describes the observer pattern

 

In the previous graph we can see that there are red data points, which represent the available data sets. The goal of regressions, is to find a line or a hyper-poli-line that best describes the observer pattern.

Now that we have a god understanding of Random Forest Algorithm, lets implement it!

Build and implement a Random Forest Algorithm

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