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A good toy model for learning to configure predictive tools.
With so few observations, there really isn't much predictive power and very high likelihood of overfitting.
In my solution, I also added some model comparison tools to the analysis.
Model Comparison
Spoiler
By accuracy alone, both models appear to be doing a good job, however, they are both quite poor predictors.
Both models predict 'No' for every observation. The Recall is 0% and Precision is NaN (0/0).
Hence Accuracy_Yes is 0 and F1 (a function of Precision) can't be computed.
By accuracy alone, both models appear to be doing a good job, however, they are both quite poor predictors.
Both models predict 'No' for every observation. The Recall is 0% and Precision is NaN (0/0).
Hence Accuracy_Yes is 0 and F1 (a function of Precision) can't be computed.
A great challenge - this was a good illustration of using predictive tools. Thank you.
Interesting predictions...
Spoiler
So we use the post race statistics to predict? Smells like post-hoc analysis in here
with scores that low, you'd expect that everybody loses in this race
with scores that low, you'd expect that everybody loses in this race
