Practice Training Process (6.3.1) - Machine Learning Basics

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9 questions

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4 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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3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is a training set?
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Think about the data you input into the model.
Question 2.
Why do we need a validation set?
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Consider how we adjust the model's performance.
3Question 3.
What is the primary purpose of a training set?
- To evaluate model performance
- To fine-tune model parameters
- To train the model
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Remember the role of each dataset.
4Question 4.
Is it true that using the same data for training and testing can lead to misleading results?
- True
- False
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Think about unbiased evaluations.
Question 5.
Describe the steps you would take to train a machine learning model. Include the importance of each dataset.
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Think about why it’s necessary to train on one dataset and evaluate on another.
Question 6.
Explain how model adjustments during training can impact a model's performance on unseen data.
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Consider the trade-off between fitting the training set well and performing on new data.

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