A business stakeholder wants to use a very high-degree Polynomial Regression for forecasting, arguing that it fits the historical data perfectly. How would you explain the risks of this approach and suggest a more robust method?
- Encourage the high-degree approach
- Explain the risk of overfitting and suggest using cross-validation or regularization
- Focus only on training data
- Ignore the stakeholder's suggestion
The high-degree approach is prone to overfitting and may not generalize well to future data. Explaining this risk and suggesting more robust methods such as cross-validation or regularization can help in building a more reliable forecasting model.
Loading...
Related Quiz
- In the context of text classification, Naive Bayes often works well because it can handle what type of data?
- What are some alternative methods to the Elbow Method for determining the number of clusters in K-Means?
- Imagine you have a model suffering from high bias. What changes would you make to the regularization techniques used?
- In a neural network, what are the nodes that receive input data and pass it forward called?
- How can Cross-Validation help in hyperparameter tuning?