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| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation and Deployment | - Model assessment metrics
|
| Topic 2: Supervised Machine Learning Models | - Regression and classification models
|
| Topic 3: Machine Learning Pipelines in SAS Viya | - Pipeline construction
|
| Topic 4: Data Preparation for Machine Learning | - Feature engineering
|
Question 1
What is the difference between a classification problem and a regression problem in machine learning?
A. There is no difference; the terms are used interchangeably.
B. Regression predicts categorical outcomes, while classification predicts numeric outcomes.
C. Classification is a type of regression problem.
D. Classification predicts categorical outcomes, while regression predicts numeric outcomes.
Question 2
What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?
A. To measure the dissimilarity between predicted and actual class probabilities
B. To quantify the variance of a model
C. To calculate the mean squared error of a regression model
D. To evaluate feature importance
Question 3
In model assessment, what is the purpose of feature importance analysis?
A. To visualize data distribution
B. To create synthetic features
C. To evaluate the significance of input features in making predictions
D. To assess data quality
Question 4
In machine learning, what does "overfitting" refer to?
A. A model that performs well on new, unseen data
B. A model that is unable to make predictions
C. A model that has too much complexity and fits the training data too closely
D. A model that is undertrained and has high bias
Question 5
In the context of model assessment, what does "bias" refer to?
A. The process of feature engineering
B. The simplicity of the model
C. A measure of the model's precision
D. A systematic error that causes a model to consistently overpredict
Solutions:
| Question 1 Answer: D | Question 2 Answer: A | Question 3 Answer: C | Question 4 Answer: C | Question 5 Answer: D |
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