최신 A00-406 무료덤프 - SASInstitute SAS® Viya® Supervised Machine Learning Pipelines

A. Confusion Matrix

B. F1 Score

C. Precision

D. Mean Absolute Error (MAE)

A. To evaluate feature importance

B. To quantify the variance of a model

C. To measure the dissimilarity between predicted and actual class probabilities

D. To calculate the mean squared error of a regression model

A. Data preprocessing and cleaning

B. Model training and evaluation

C. Data storage and retrieval

D. Data visualization

A. Data that is encrypted for security

B. Data that is stored in a physical format

C. Data that is in a non-standard, proprietary format

D. Data about data, providing information such as data source, structure, and context

A. A model's inability to generalize to new data

B. The overall accuracy of a model

C. The simplicity of a model

D. Systematic errors that cause a model to consistently underpredict or overpredict

A. Real-time data analysis

B. Data exploration and visualization

C. Long-term data storage and analysis

D. Data transformation and cleaning

A. Encrypting data to protect against unauthorized access

B. Compressing data to reduce storage space

C. Storing multiple copies of the same data to increase redundancy

D. Keeping track of different versions or changes to data over time

A. They are simple and easy to interpret.

B. They combine multiple models to improve predictive performance.

C. They require minimal data preprocessing.

D. They are not suitable for large datasets.

A. Creating new features or transforming existing ones to improve model performance

B. Model evaluation

C. Data visualization

D. Data preprocessing

A. One-Hot Encoding

B. Standardization

C. Principal Component Analysis (PCA)

D. Decision Trees