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Free AI-900 Exam Braindumps (page: 14)

Page 13 of 63
PDF with 247 Questions

HOTSPOT (Drag and Drop is not supported)
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset
Regression is a machine learning task that is used to predict the value of the label from a set of related features.


Reference:

https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks



HOTSPOT (Drag and Drop is not supported)
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: No
The validation dataset is different from the test dataset that is held back from the training of the model.

Box 2: Yes
A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model's hyperparameters.

Box 3: No
The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.


Reference:

https://machinelearningmastery.com/difference-test-validation-datasets/



What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution.
Note: Each correct selection is worth one point.

  1. coefficient of determination (R2)
  2. F1 score
  3. root mean squared error (RMSE)
  4. area under curve (AUC)
  5. balanced accuracy

Answer(s): A,C

Explanation:

A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Incorrect Answers:
B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model.
D: aucROC or area under the curve (AUC) is used to evaluate a classification model.


Reference:

https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics



HOTSPOT (Drag and Drop is not supported)
To complete the sentence, select the appropriate option in the answer area.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Regression is a machine learning task that is used to predict the value of the label from a set of related features.


Reference:

https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks






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