yardstick | Mirror of the OPNFV Yardstick Project

I have something similar to the following example:

I'd like to calculate precision and recall and create a precision-recall plot in R from a binary dataset (did an event occur/not occur). My data is set out similar to the below example.

I am using tidymodels for building a model where false negatives are more costly than false positives. Hence I'd like to use the yardstick::classification_cost metric for hyperparameter tuning, but with a custom classification cost matrix that reflects this fact.

Doing this after fitting a model is simple enough:

I want to use purrr::map_* functions to extract info from multiple models involving linear regression method. I am first creating some random dataset. The dataset has three dependent variables, and one independent variable.

WHAT I WANT: I'm trying to fit a GAM model for classification using tidymodels on a given data.

SO FAR: I'm able to fit a logit model.

I am working on an assignment where I have to evaluate the predictive model based on RMSE (Root Mean Squared Error) using the test data. I have already built a linear regression model to predict wine quality (numeric) using all available predictor variables based on the train data. Below is my current code. The full error is "Error: Problem with mutate() column regression1. i regression1 = predict(regression1, newdata = my_type_test). x no applicable method for 'predict' applied to an object of class "c('double', 'numeric')"

I am building a custom recipes function and getting an error when I try to prep() the recipe. I get the following error:

I ran the the following models properly and I need to choose the best two (for one or more metrics). The difference between models are the recipes objects that take differents steps for unbalanced data (without, smote, rose, upsample, step_adasyn). I am interesting in select more than one, the best two and also select by unbalanced function.

I am trying to use purrr to map multiple functions to two inputs. An example is given below but ideally i'd like to extend this to more functions. When trying to do this i'm getting an error that the input is not found, however, even when I try and name inputs in the list of functions this doesn't rectify the problem.