Question 6Polynomial features capture what effects?1 pointNon-linear effects. Linear effects.Multiple effects. Regression effects

Understanding Polynomial Features in Machine Learning

Polynomial features are used in machine learning to enhance the model's ability to capture complex relationships in data. They permit algorithms to create non-linear relationships by introducing terms that are powers of the original features.

1. Non-linear Effects: Polynomial features primarily capture non-linear effects by allowing the model to fit curves instead of just linear lines. For example, instead of fitting a straight line through points on a graph, polynomial regression can fit a parabolic curve, capturing the trend more accurately.

2. Linear Effects: While polynomial features can include linear effects (e.g., the original features themselves when raised to the first power), they are not specifically designed to capture linear relationships.

3. Multiple Effects: Polynomial features can also capture interactions between different features when used in a multivariate environment. However, this factor is secondary to their ability to capture non-linear relationships.

4. Regression Effects: This term is quite broad and does not specify the nature of the effects captured; it simply relates to the statistical techniques used.

Conclusion

The most accurate description of what polynomial features capture is non-linear effects, as they expand the feature space with higher-degree terms, enabling the model to fit more complex relationships in the data. Therefore, the best answer is Non-linear effects.