Description Usage Arguments Value Author(s) Examples
Takes a list of trained machine learning models and returns diagnostics as a data frame as to compare the effectiveness of algorithms. Measures include Accuracy, Prevalence, Detection Rate, F1, Cohen's Kappa, McNemar P-Value, Negative and Positive Predictive value, Precision, Recall, Sensitivity, and Specificity
1 | make_table(models, test_x, test_y)
|
models |
A list of models of class 'train' |
test_x |
'data.frame' or 'tibble'. explanitory variables |
test_y |
'vector' target variable |
This function returns a data.frame
including columns:
Type
Accuracy - Overall performance of model
Accuracy Lower
Accuracy Null
Accuracy P value
Accuracy Upper
Balanced Accuracy
Detection Prevalence
Detection Rate
F1 - Hybrid metric usefull fr unbalanced classes
Kappa - Compares an observed accuracy with an expected accuracy
McNemar P Value
Negagive Prediction Value
Positive Predictive Value
Precision - How accurate the positive predictions are
Prevalence
Recall - True positive rate, number of instances from the positive class that actually predictoed correctly
Sensitivity - Same as recall
Specificty - Number of instances from the negative class that were actually predicted correctly
Method the algorithm used to train each particular model
"Chad Schaeffer <sch12059@byui.edu>
1 2 3 4 | ## Not run:
make_table(models_list, test_x, test_y)
## End(Not run)
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