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Question 180 - MLS-C01 discussion

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A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

A.
Increase the max_depth parameter value.
Answers
A.
Increase the max_depth parameter value.
B.
Lower the max_depth parameter value.
Answers
B.
Lower the max_depth parameter value.
C.
Update the objective to binary:logistic.
Answers
C.
Update the objective to binary:logistic.
D.
Lower the min_child_weight parameter value.
Answers
D.
Lower the min_child_weight parameter value.
Suggested answer: B

Explanation:

Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit.According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting.The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1.References:

XGBoost Parameters

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asked 16/09/2024
PKE Holding AG Leitgeb
36 questions
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