Podcast
Questions and Answers
What optimization objective maximizes the area under the receiver operating characteristic curve (AUC ROC) value?
What optimization objective maximizes the area under the receiver operating characteristic curve (AUC ROC) value?
- Minimizing the area under the precision-recall curve
- Maximizing the area under the receiver operating characteristic curve (correct)
- Maximizing the area under the precision-recall curve
- Minimizing the area under the receiver operating characteristic curve
In the context of a video sharing website, what is a good measure to determine the success of an ML model predicting popular videos?
In the context of a video sharing website, what is a good measure to determine the success of an ML model predicting popular videos?
- Predicting 95% of the most popular videos measured by watch time within 30 days of being uploaded (correct)
- The Pearson correlation coefficient between log-transformed views after 7 and 30 days is equal to 0
- Predicting videos as popular if the user who uploads them has over 10,000 likes
- Predicting 97.5% of the most popular clickbait videos measured by number of clicks
What should be done to assist gradient optimization when dealing with columns of data having different ranges in a Neural Network project?
What should be done to assist gradient optimization when dealing with columns of data having different ranges in a Neural Network project?
- Use representation transformation (normalization) technique (correct)
- Utilize Lasso regression analysis for feature selection
- Improve data cleaning by removing features with missing values
- Use feature construction to combine the strongest features
What method can be used to eliminate features that do not provide a strong signal in a model?
What method can be used to eliminate features that do not provide a strong signal in a model?
Which Google Cloud tool can be used to determine the impact of excluding individual features on model performance?
Which Google Cloud tool can be used to determine the impact of excluding individual features on model performance?
What function in BigQuery can be used to calculate the Pearson correlation coefficient between features and the target variable?
What function in BigQuery can be used to calculate the Pearson correlation coefficient between features and the target variable?
In a highly skewed dataset where examples without the company's logo are dominant, which metric would be most suitable to evaluate a classification model?
In a highly skewed dataset where examples without the company's logo are dominant, which metric would be most suitable to evaluate a classification model?
Which metric places more emphasis on the ability of the model to capture all positive instances, given a dataset highly imbalanced towards one class?
Which metric places more emphasis on the ability of the model to capture all positive instances, given a dataset highly imbalanced towards one class?
What technique can be used to obtain feature attributions from a model by submitting prediction requests with a specific keyword?
What technique can be used to obtain feature attributions from a model by submitting prediction requests with a specific keyword?
What approach should you take when building a regression model to minimize effort and training time while maximizing model performance?
What approach should you take when building a regression model to minimize effort and training time while maximizing model performance?
In a linear model with over 100 input features, all with values between –1 and 1, what technique is suitable to remove non-informative features?
In a linear model with over 100 input features, all with values between –1 and 1, what technique is suitable to remove non-informative features?
If you suspect that many features are non-informative in your model, what should you do to keep the informative ones while removing the non-informative ones?
If you suspect that many features are non-informative in your model, what should you do to keep the informative ones while removing the non-informative ones?
When building a regression model, what method can you use to identify which features do not degrade the model when removed?
When building a regression model, what method can you use to identify which features do not degrade the model when removed?
In a high-dimensional dataset with potentially non-informative features, what strategy helps differentiate informative from non-informative features?
In a high-dimensional dataset with potentially non-informative features, what strategy helps differentiate informative from non-informative features?
Which method is most suitable for identifying the least informative features in a regression model and discarding them?
Which method is most suitable for identifying the least informative features in a regression model and discarding them?
