What is the main goal of model selection in predictive analytics?

The primary goal of model selection in predictive analytics is to improve model accuracy. In this context, model selection involves evaluating different algorithms and approaches to identify the one that best captures the underlying patterns of the data, leading to better predictive performance. By selecting a model that generalizes well to unseen data, practitioners aim to enhance the overall accuracy of their predictions, thus providing more reliable and valid insights.

While maintaining data integrity, enhancing computational efficiency, and simplifying model complexity are important considerations in the modeling process, they are not the main focus of the model selection phase. Instead, the primary objective is to choose a model that optimally fits the data while maintaining a balance between complexity and predictive accuracy, ensuring that it performs well not just on the training set but also on new, unseen data.