Practice Applications (5.4.3) - Supervised Learning

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
List one application of XGBoost in real-world scenarios.
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Think about where data scientists collaborate for challenges.
Question 2.
What does XGBoost stand for?
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Focus on the 'X' part of XGBoost.
3Question 3.
Which application is NOT typically associated with XGBoost?
- Kaggle Competitions
- Healthcare Diagnosis
- Social Media Marketing
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Think about the typical fields of data science.
4Question 4.
True or False: XGBoost is a type of neural network.
- True
- False
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Recall the differences between these types of algorithms.
Question 5.
In a Kaggle competition, a team used XGBoost to predict customer churn in a subscription model. What steps would you recommend they take to optimize their model?
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Think about methods to improve model accuracy.
Question 6.
Discuss how XGBoost can improve healthcare predictive analytics when dealing with high dimensional datasets.
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Consider how noise affects predictions.

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