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Listening Quiz: Giải Thích Kết Quả Kiểm Định Chéo Và Lựa Chọn Mô Hình – Easy Level
Th1 26, 2025
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bởi
Anna Phạm
trong
Tiếng Anh dành cho Nhà khoa học dữ liệu (English for Data Scientist)
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1.
What is the main topic of the conversation?
Why accuracy is always the only score used
How to split data into random groups
How to build a model from scratch
How cross-validation helps evaluate models and choose the best one
Explanation:
Mark explains cross-validation, how it is used to check model performance, and how model selection is based on cross-validation scores.
2.
According to Mark, how do they perform cross-validation?
By splitting data into parts, then training on some and testing on others
By comparing models without using data
By testing the model only on the training data
By choosing the model with the lowest error rate before training
Explanation:
Mark says they split the data into parts, train on some parts, and test on others.
3.
Why do they split the data, according to the conversation?
To remove all errors from the model
To avoid using scores
To make the dataset larger
To see if the model is good for new data
Explanation:
Mark explains that splitting the data helps show whether the model works well on new data.
4.
Which measure is mentioned as one of the scores used in model selection?
Accuracy
Memory usage
Data size
Speed
Explanation:
Mark says they use accuracy, error rate, or other measures.
5.
What can be inferred about model selection from the conversation?
The model with the worst score is preferred
The model is chosen only by guesswork
Cross-validation is used after the model is already fixed forever
The model with the best cross-validation score is chosen
Explanation:
Mark says they pick the best model based on scores from cross-validation, which means the best score helps determine the choice.
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