Academic Writing on Random Forest and XGBoost.

Explain the following topics in 7 to 12 pages.

*The topic is on CLASSIFICATION decision trees*

- Explain how a decision tree works (in detail): include how are the splits done, what metrics are taken in consideration. How is impurity determined?

- Main characteristic of a decision tree and define the most important hyperparameters

- Explain bagging vs boosting technique

- Explain the concept of the random forest algorithm

- Explain the concept of the extreme gradient boosting (XGBoost) algorithm

- Discuss why random forests and XGBoost models easily overfit to the data.

- Explain the importance of hyperparameter tuning and mention the most important hyperparameters

Important remarks:

- There should be references using the APA format and cite accordingly.

- You should use mathematical formulations when applicable

- Any time of pictures/images is welcome as long as it is cited and referenced accordingly.

- Provide examples when applicable

Evner: Research Writing, Research, Teknisk Forfatning, Machine Learning (ML), Statistikker

Om klienten:
( 8 bedømmelser ) A Coruna, Spain

Projekt ID: #33751962

Tildelt til:


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