A data scientist is building a model to predict customer credit scores based on information collected from reporting agencies. The model needs to automatically adjust its parameters to adapt to…

The correct answer is D. XGBoost. XGBoost supports "warm-start" incremental training, continuing to refine the existing ensemble with new data, so it can automatically update its parameters as new agency information arrives. The other methods require full retraining to incorporate recent changes.

Machine Learning

Question

A data scientist is building a model to predict customer credit scores based on information collected from reporting agencies. The model needs to automatically adjust its parameters to adapt to recent changes in the information collected. Which of the following is the best model to use?

Options

ADecision tree
BRandom forest
CLinear discrimination analysis
DXGBoost

How the community answered

(45 responses)

A
13% (6)
B
7% (3)
C
22% (10)
D
58% (26)

Explanation

XGBoost supports "warm-start" incremental training, continuing to refine the existing ensemble with new data, so it can automatically update its parameters as new agency information arrives. The other methods require full retraining to incorporate recent changes.

Topics

#XGBoost#gradient boosting#adaptive learning#credit scoring

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