XGBoost is the shortened form of eXtreme Gradient Boosting and is used to push the boundaries of the limits of machine computation in order to make them more accurate and portable for large decision-making charts.

It is a highly efficient means of computation that can be used as a prediction tool in forecasting many outcomes from a system like in Forex trading. It can be used to plot decision trees that is essential in collating and arranging data from several sources like the data of a large number of people.

Related Course:
Deep Learning with TensorFlow 2 and Keras


XGBoost can help in categorizing information from several sources and compiling them in order to make them more accurate.

XGBoost makes it easy to scale any form of data due to its vey complex algorithm that constantly learns and relearns over time.

Foor example if a sick person goes to a doctor for aid and the doctor provides just enough information to help but not a total cure, the patient can decide to visit several other doctors for more information.

However XGBoost can be used in this scenario to collate all the various data from the different doctors and by comparing all their information it can accurately make a decision that is the most optimum and best for the patient.

In the scenario listed above, the XGBoost is able to identify instances where the doctors were unable to provide a solution and use the information from another doctor to compensate for the lack of complete information from the first doctor.

This form of analysis is applicable in other aspects and makes it easy for the user to see how a computer arrives at a decision without skipping any bits.