Random forests are a popular supervised machine learning algorithm. Random forests are for supervised machine learning, where there is a labeled target variable. Random forests can be used for solving regression (numeric target variable) and classification (categorical target variable) problems. Random forests are an ensemble method, meaning they combine predictions from other models. Each of the smaller models in the random forest ensemble is a decision tree.

Data Features

Random forest classifer is implemented in sklearn Python module and data features is feeded into it. Data features are computed as geometrical and volumtrical features using KNN of points regarding multiple search radiuses. To improve computational cost, the features are computed in Cython language, which is faster than Python and its structure is closer to C language.