Select ALL true statements regarding the ML EVALUATE function
Representing Features questions >>> Select ALL true statements regarding the ML EVALUATE function >>> Feature Engineering
1.
Question 1
Select ALL true statements regarding the ML.EVALUATE function?
1 / 1 point
The ML.EVALUATE function evaluates the predicted values against the actual data.
You can use the ML.EVALUATE function to evaluate model metrics.
All of the above.
4.
Question 4
Which of the following is true about Feature Cross?
1 / 1 point
It is a process of combining features into a single feature.
Feature Cross enables a model to learn separate weights for each combination of features.
Both A and B
None of the above
2.
Question 2
What is the significance of ML.FEATURE_CROSS?
1 / 1 point
ML.FEATURE_CROSS generates a STRUCT feature with all combinations of crossed categorical features except for 1-degree items.
ML.FEATURE_CROSS generates a STRUCT feature with few combinations of crossed categorical features except for 1-degree items.
ML.FEATURE_CROSS generates a BUCKET feature with all combinations of crossed categorical features including 1-degree items.
ML.FEATURE_CROSS generates a BUCKET STRUCT feature with few combinations of crossed categorical features except for 1-degree items.
5.
Question 5
Which of the following statements is incorrect?
1 point
When we do feature crosses, we run into the risk of overfitting
We use the Regularization process in order to prevent overfitting.
BQML by default assumes that numbers are numeric features and strings are categorical features.
None of the above
3.
Question 3
Select ALL true statements regarding the ML.BUCKETIZE function?
1 / 1 point
ML.BUCKETIZE is a pre-processing function that creates buckets by returning a STRING as the bucket name after numerical_expression is split into buckets by array_split_points.
It bucketizes a continuous numerical feature into a string feature with bucket names as the value.