Podcast
Questions and Answers
What is the recommended percentage split for training and testing data?
What is the recommended percentage split for training and testing data?
- 80% for training and 20% for testing
- 70% for training and 30% for testing (correct)
- 50% for training and 50% for testing
- 60% for training and 40% for testing
Why is it important to use new data when evaluating a model?
Why is it important to use new data when evaluating a model?
- To train the model on a larger dataset
- To increase the model's complexity
- To prevent overfitting to the training set (correct)
- To speed up the evaluation process
What is the purpose of a validation set in machine learning?
What is the purpose of a validation set in machine learning?
- To train the model on additional data
- To evaluate the model while building and tuning it (correct)
- To measure the model's performance on the training data
- To use as the final test set
Why should the model not be trained on the entire dataset?
Why should the model not be trained on the entire dataset?
What risk is associated with using the test set to select model parameters?
What risk is associated with using the test set to select model parameters?
What happens if the model is tuned based on performance only on the test data?
What happens if the model is tuned based on performance only on the test data?
Why is squared error commonly used in machine learning?
Why is squared error commonly used in machine learning?
What does the R2 coefficient represent in machine learning?
What does the R2 coefficient represent in machine learning?
What happens when a machine learning model has high bias?
What happens when a machine learning model has high bias?
What is the purpose of validation curves in machine learning?
What is the purpose of validation curves in machine learning?
What does a gap between the training and validation error in learning curves indicate?
What does a gap between the training and validation error in learning curves indicate?
What is a common consequence of models with high variance?
What is a common consequence of models with high variance?
How can models suffering from high bias be improved?
How can models suffering from high bias be improved?
What is one common use of reducing a dataset into two dimensions when evaluating a classifier model?
What is one common use of reducing a dataset into two dimensions when evaluating a classifier model?
Which region in a validation curve indicates that a model is subject to high bias?
Which region in a validation curve indicates that a model is subject to high bias?
What does underfitting refer to in machine learning?
What does underfitting refer to in machine learning?
What is an appropriate approach for improving models that suffer from high variance?
What is an appropriate approach for improving models that suffer from high variance?
When will training on more data do very little to improve a model with high bias?
When will training on more data do very little to improve a model with high bias?
What percentage of the data is typically used for training in a train/test/validation split?
What percentage of the data is typically used for training in a train/test/validation split?
Which metric is defined as the percentage of correct predictions for the test data?
Which metric is defined as the percentage of correct predictions for the test data?
What fraction is precision defined as?
What fraction is precision defined as?
In which scenario is recall important?
In which scenario is recall important?
What is the common approach for combining precision and recall metrics?
What is the common approach for combining precision and recall metrics?
Why do we have a different set of evaluation metrics for regression models compared to classification models?
Why do we have a different set of evaluation metrics for regression models compared to classification models?
What does explained variance metric represent?
What does explained variance metric represent?
What does mean squared error measure?
What does mean squared error measure?
Which metric compares the variance within the expected outcomes to the variance in the error of a regression model?
Which metric compares the variance within the expected outcomes to the variance in the error of a regression model?
Which parameter allows us to control the tradeoff of importance between precision and recall?
Which parameter allows us to control the tradeoff of importance between precision and recall?
What should be done before making splits in a train/test/validation scenario to ensure an accurate representation of the dataset?
What should be done before making splits in a train/test/validation scenario to ensure an accurate representation of the dataset?
Why are precision and recall useful in cases where classes aren't evenly distributed?
Why are precision and recall useful in cases where classes aren't evenly distributed?
