advanced learning algorithms coursera week 3 answers

Practice quiz: Advice for applying machine learning

1. In the context of machine learning, what is a diagnostic?

  • A process by which we quickly try as many different ways to improve an algorithm as possible, so as to see what works.
  • This refers to the process of measuring how well a learning algorithm does on a test set (data that the algorithm was not trained on).
  • An application of machine learning to medical applications, with the goal of diagnosing patients’ conditions.
  • A test that you run to gain insight into what is/isn’t working with a learning algorithm.

2. True/False? It is always true that the better an algorithm does on the training set, the better it will do on generalizing to new data.

  • True
  • False

3. For a classification task; suppose you train three different models using three different neural network architectures. Which data do you use to evaluate the three models in order to choose the best one?

  • The training set
  • The test set
  • The cross validation set
  • All the data — training, cross validation and test sets put together.

Practice quiz: Bias and variance

4. If the model's cross validation error J cv ​ is much higher than the training error J train ​ , this is an indication that the model has…

  • high bias
  • high variance
  • Low variance
  • Low bias

5. Which of these is the best way to determine whether your model has high bias (has underfit the training data)?

  • See if the cross validation error is high compared to the baseline level of performance
  • Compare the training error to the cross validation error.
  • Compare the training error to the baseline level of performance
  • See if the training error is high (above 15% or so)

6. You find that your algorithm has high bias. Which of these seem like good options for improving the algorithm’s performance? Hint: two of these are correct.

  • Collect additional features or add polynomial features
  • Remove examples from the training set
  • Decrease the regularization parameter (lambda)
  • Collect more training examples

7. You find that your algorithm has a training error of 2%, and a cross validation error of 20% (much higher than the training error). Based on the conclusion you would draw about whether the algorithm has a high bias or high variance problem, which of these seem like good options for improving the algorithm’s performance? Hint: two of these are correct.

  • Decrease the regularization parameter
  • Reduce the training set size
  • Increase the regularization parameter
  • Collect more training data

Practice quiz: Machine learning development process

8. Which of these is a way to do error analysis?

  • Calculating the training error
  • Manually examine a sample of the training examples that the model misclassified in order to identify common traits and trends.
  • Collecting additional training data in order to help the algorithm do better.
  • Calculating the test error

9. We sometimes take an existing training example and modify it (for example, by rotating an image slightly) to create a new example with the same label. What is this process called?

  • Data augmentation
  • Machine learning diagnostic
  • Error analysis
  • Bias/variance analysis

10. What are two possible ways to perform transfer learning? Hint: two of the four choices are correct.

  • Given a dataset, pre-train and then further fine tune a neural network on the same dataset.
  • You can choose to train all parameters of the model, including the output layers, as well as the earlier layers.
  • Download a pre-trained model and use it for prediction without modifying or re-training it.
  • You can choose to train just the output layers’ parameters and leave the other parameters of the model fixed.

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