The bagging process repeated B times with selecting a random sample by changing the training set and, tries to fit the relevant tree algorithms to the samples. We would take for training sample, X = x 1, …, x n and, Y = y 1, …, y n for the outputs. The random forest uses bootstrap aggregating( bagging) algortihms. This increases the performance of the final model, although this situation creates a small increase in bias. The random forest algorithms average these results that is, it reduces the variation by training the different parts of the train set. This algorithm is more robust to overfitting than the classical decision trees. Random Forest for regression, constructs multiple decision trees and, inferring the average estimation result of each decision tree. This algorithm also has a built-in function to compute the feature importance. Of course, we will also add the funding rates variable, the president mentioned, to the model to compare with the other explanatory variables.īecause the variables can be highly correlated with each other, we will prefer the random forest model. The most common view of the economic authorities is that the variables affecting the rates are currency exchange rates, and CDS(credit default swap). In order to check that we have to model inflation rates with some variables. And yes, unfortunately, the central bank officials have limited independence doing their job in Turkey contrary to the rest of the world. For this reason, he dismissed two central bank chiefs within a year. The Turkish president thinks that high interest rates cause inflation, contrary to the traditional economic approach.
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