We are using Google Teachable Machine for teaching ML in K-12 as part of our initiative Computação na Escola at the Federal University of Santa Catarina/Brazil and it has been demonstrated to be a very intuitive and motivating tool in this context.
Yet, in order to teach also a little more about how it works, we find it difficult to encounter documentation. Is there any more detailed documentation available?
We are also in doubt with respect to the following issues:
“Teachable Machine splits sample into training sample (85%) and “test” sample (15%). Test samples are never used to train the model, so after the model has been trained on the training samples, they are used to check how well the model is performing on a new, never-before-seen data.”
Following this definition, we assume that in fact a “test” set is separated and not a “validation” set (being used to evaluate the model performance at each training epoch)?
We didn’t find any indication that data augmentation is done, so we assume that no data augmentation is done?
It is not clear whether, at the end of the training, the result is the model of the last trained epoch or the model obtained at the epoch with the highest accuracy/lowest loss during training.
We assume that the result is the model resulting from the last training epoch? Which is then used for the performance evaluation with the test set (accuracy by category/confusion matrix)?
Thanks for any help!