Free AI-900 Exam Braindumps (page: 14)

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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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