Which of the following tasks is part of the predictive modeling process?

In the predictive modeling process, both evaluating model fit statistics and training decision trees are essential tasks that contribute to building and validating effective predictive models.

Evaluating model fit statistics involves assessing how well a predictive model performs by analyzing various metrics that measure its accuracy and effectiveness. These metrics might include measures like R-squared, root mean square error (RMSE), and classification accuracy, depending on whether the model is for regression or classification tasks. This evaluation helps in understanding the model's predictive ability and guides adjustments and refinements.

Training decision trees constitutes a fundamental step in the modeling process, where algorithms learn from historical data to identify patterns and relationships within the data. This involves splitting the data based on feature values to create a tree structure that can be utilized for making predictions on new, unseen data. The training phase is crucial because the model must learn to generalize from the training set to provide accurate predictions when applied to new data.

Since both of these tasks are integral components of the predictive modeling process, the selection that includes all tasks accurately reflects the comprehensive nature of model development. Therefore, opting for the choice that encompasses both evaluating model fit statistics and training decision trees encapsulates the complete process of predictive modeling, making it the most comprehensive answer.