R Functions for Analytics Tasks Quiz

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What method is commonly used for variable selection in linear regression to prevent overfitting?

Empirical Bayes

Geometric distribution

Box-Cox transformation

Elastic net (correct)

Which technique helps in identifying the most significant variables in a model by penalizing coefficients to zero?

Lasso regression (correct)

Principal component analysis

Factorial design

Exponential distribution

What is a common consequence of overfitting in predictive modeling?

K-nearest-neighbor classification

Cyclic/seasonal effects

Network optimization

Misleading correlation (correct)

Which model selection criterion penalizes additional complexity to prevent overfitting?

Akaike information criterion (AIC) Signup and view all the answers

How does lasso regression differ from ridge regression in terms of variable selection?

Lasso regression encourages sparsity while ridge regression discourages it. Signup and view all the answers

Which technique combines L1 and L2 penalties for variable selection and regularization?

Elastic net Signup and view all the answers

Which R function is commonly used for making predictions from models?

predict Signup and view all the answers

In predictive modeling, which R function is best suited for k-nearest-neighbor algorithm?

kknn Signup and view all the answers

When building linear regression models in R, which function should be used?

lm Signup and view all the answers

Which software package is most suitable for analyzing optimization models?

PuLP Signup and view all the answers

Why might the selected model's expected performance when forecasting the next 36 months be worse than its observed performance on the validation data set?

The real situation may have changed, making the forecasted situation different from when the model was trained. Signup and view all the answers

What common issue could arise if the selected model was chosen based on its performance on a validation set?

A biased model selection process leading to poor generalization. Signup and view all the answers

What risk does overfitting present when using a predictive model for future forecasting?

Higher accuracy on the training data but potential poor performance on unseen data. Signup and view all the answers

How does the potential presence of a selection bias affect the chosen model's forecasting capabilities?

Results in a better fit to random patterns in the validation data but may not generalize well. Signup and view all the answers

How can changes in the real situation impact the performance of a predictive model over time?

Result in differences between forecasted situations and historical training/validation data. Signup and view all the answers

What role does generalization play in evaluating a predictive model's performance for future forecasts?

It helps prevent overfitting by promoting simpler models that capture true patterns. Signup and view all the answers

R Functions for Analytics Tasks Quiz

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