XGBRF_Classifier

The XGBRF_Classifier was utilized to predict the values in the "pred" column of a dataset consisting of two categorical columns (pc, ma) and sixteen numerical columns (m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14). To handle missing values, the columns ('ld', 'm0', 'm1', 'm2', 'm3', 'm4', 'm5', 'm6', 'm7') were imputed with the median value, while the remaining columns ('m8', 'm9', 'm10', 'm11', 'm12', 'm13', 'm14') were imputed with the mean value using a simple imputer. The XGBRFClassifier is an implementation of the Random Forest algorithm using the XGBoost framework. It combines the strength of both Random Forests and gradient boosting to achieve high performance and robustness in handling complex datasets. It creates an ensemble of decision trees and uses gradient boosting techniques to optimize the individual trees' performance. The predictions generated by the XGBRF_Classifier were saved in a CSV file named "submission.csv." This file contains the predicted values for the "pred" column based on the input data and the trained XGBRF_Classifier.