tidyAML Updates

Steven Sanderson has been busy. First up, a post on tidyAML updates:

One of the standout features in this release is the addition of extract_regression_residuals(). This function empowers users to delve deeper into regression models, providing a valuable tool for analyzing and understanding residuals. Whether you’re fine-tuning your models or gaining insights into data patterns, this enhancement adds a crucial layer to your analytical arsenal.

Then, Steven goes into detail on .drap_na:

In the newest release of tidyAML there has been an addition of a new parameter to the functions fast_classification() and fast_regression(). The parameter is .drop_na and it is a logical value that defaults to TRUE. This parameter is used to determine if the function should drop rows with missing values from the output if a model cannot be built for some reason. Let’s take a look at the function and it’s arguments.

After that, we get to see an updated function:

In response to user feedback, we’ve enhanced the internal_make_wflw_predictions() function to provide a comprehensive set of predictions. Now, when you make a call to this function, it includes:

1. The Actual Data: This is the real-world data that your model aims to predict. Having access to this information helps you assess how well your model is performing on unseen instances.
2. Training Predictions: Predictions made on the training dataset. This is essential for understanding how well your model generalizes to the data it was trained on.
3. Testing Predictions: Predictions made on the testing dataset. This is crucial for evaluating the model’s performance on data it hasn’t seen during the training phase.

You can also check out the package’s GitHub repository and see more.