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Random Forest is widely used in a variety of fields, and its flexibility makes it suitable for both classification and regression problems. In a classification task, the model decides which of the predefined classes the current state belongs to. For example, in the financial market, this could mean a decision to buy (class 1) or sell (class 0) an asset based on a variety of indicators.

However, in this article, we will focus on regression problems. Regression in machine learning is an attempt to predict the future numerical values of a time series based on its past values. Instead of classification, where we assign objects to certain classes, in regression we aim to predict specific numbers. This could be, for example, forecasting stock prices, predicting temperature or any other numerical variable.

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