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Exam PA (Predictive Analytics)
#Question
Which regression technique penalizes large coefficients to reduce model complexity?
A.
Stepwise Regression
B.
Lasso Regression
C.
Multiple Regression
D.
Ridge Regression
Correct Answer:
D
Ridge regression adds a penalty for large coefficients to reduce model complexity and manage multicollinearity.
#Question
When is bootstrapping particularly beneficial?
A.
Time series data
B.
Limited datasets
C.
Large datasets
D.
Unstructured data
Correct Answer:
B
Bootstrapping allows for robust statistical analysis by resampling limited datasets, enhancing estimates of accuracy.
#Question
What could be a consequence of failing to preprocess data before modeling?
A.
Increased noise
B.
Data consistency
C.
Better insights
D.
Improved accuracy
Correct Answer:
A
Failing to preprocess data can leave noise and inconsistencies in the dataset, negatively affecting model performance.
#Question
A company wants to predict customer retention based on service usage. What would be ideal?
A.
A/B Testing
B.
Decision Trees
C.
Time Series Forecasting
D.
Logistic Regression
Correct Answer:
D
Logistic regression is appropriate for predicting binary outcomes, such as whether a customer will remain or leave.
#Question
In predictive analytics, what does "data drift" refer to?
A.
Increased data volume
B.
Constant data quality
C.
Changes in prediction over time
D.
Regular data patterns
Correct Answer:
C
Data drift refers to significant changes in data distributions over time, which can deteriorate model performance.
#Question
How can feature interaction impact a predictive model?
A.
Simplifies understanding
B.
Enhances modeling power
C.
Increase bias
D.
Hinders performance
Correct Answer:
B
Feature interactions can capture combined effects between variables, enhancing predictive power in models.
#Question
In regression analysis, which term describes the difference between the observed and predicted values?
A.
Residual
B.
Prediction
C.
Error
D.
Bias
Correct Answer:
A
Residuals represent the differences between observed values and predictions made by the regression model.
Exam PA (Predictive Analytics)
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