I looked at the GitHub README for the project, which says
> Uses machine learning to classify Twitter accounts as bots or not bots. The default model is 93.53% accurate when classifying bots and 95.32% accurate when classifying non-bots. The fast model is 91.78% accurate when classifying bots and 92.61% accurate when classifying non-bots.
Overall, the default model is correct 93.8% of the time.
Overall, the fast model is correct 91.9% of the time.
How is this accuracy determined? There is no information available explaining how this determination is quantified, nor what the caveats are.
I agree this is probably what happened. From the estimation object I see around a total of 3500 training samples with around 90 predictors. 3-fold cross-validation was done so at each iteration only around 2150 samples were used for training... And it seems none of the samples were used for out-of-sample checking.
> Uses machine learning to classify Twitter accounts as bots or not bots. The default model is 93.53% accurate when classifying bots and 95.32% accurate when classifying non-bots. The fast model is 91.78% accurate when classifying bots and 92.61% accurate when classifying non-bots.
Overall, the default model is correct 93.8% of the time.
Overall, the fast model is correct 91.9% of the time.
How is this accuracy determined? There is no information available explaining how this determination is quantified, nor what the caveats are.