最新的SASInstitute SAS® Viya® Supervised Machine Learning Pipelines - A00-406免費考試真題

A. Min samples split

B. Learning rate

C. Max features

D. Max depth

A. To prepare the data for analysis and modeling

B. To train the model

C. To visualize the data

D. To remove irrelevant features

A. To visualize data distribution

B. To evaluate the mean squared error of a model

C. To measure feature importance

D. To compare models' performance in terms of sensitivity and specificity

A. Precision

B. Confusion Matrix

C. F1 Score

D. Mean Absolute Error (MAE)

A. It represents the trade-off between underfitting and overfitting.

B. It indicates the trade-off between accuracy and precision.

C. It refers to the trade-off between the number of features and the model's complexity.

D. It is not relevant in machine learning.

A. To ensure the model generalizes well to new, unseen data

B. To assess the accuracy of the model

C. To create synthetic data

D. To build a model from scratch

A. Making predictions or inferences from data

B. Feature engineering

C. Data visualization

D. Data cleaning

A. F1 score is applicable to a model with a binary target.

B. F1 score is calculated based on a cut off value.

C. F1 score is applicable to a model with an interval target.

D. F1 score is calculated based on a depth value.

A. Efficient Text Link

B. Examine, Test, Log

C. Execute, Terminate, Launch

D. Extract, Transform, Load

A. To visualize data distribution

B. To assess data quality

C. To create synthetic features

D. To evaluate the significance of input features in making predictions