Model Settings can be an intimidating or confusing step in the training process for those not well versed in machine learning, but are an important part of creating an accurate and efficient model. So we're here to break down what each setting means and how it all works together to make the best model for your use case.
There are a couple of considerations to balance when training models.
Tuning the model settings is an experimental process of trial and error. While there are defaults provided from our team, to create the best model for your use case you'll need to try at least a few combinations of different settings to tune the model.
At the end of each run you can you'll need to evaluate your model's outputs to understand where you need to focus your next run. Classification Models have multiple categorical outputs. Most error measures will calculate the total error in our model, but we cannot find individual instances of errors in our model. The model might misclassify some categories more than others, but we cannot see this using a standard accuracy measure. Furthermore, suppose there is a significant class imbalance in the given data. In that case, i.e., a class has more instances of data than the other classes, a model might predict the majority class for all cases and have a high accuracy score; when it is not predicting the minority classes. This is where confusion matrices are useful.
A confusion matrix presents a table layout of the different outcomes of the prediction and results of a classification problem and helps visualize its outcomes.
It plots a table of all the predicted and actual values of a classifier.
You can access the Confusion Matrix by opening up the model drawer and clicking on the model.
Based on your use case, you may be okay with a bias toward positive or negative identification. Focus your time on where your model is performing relative to your use case, not trying to optimize it to be perfect (unless you need or are into that.)
As you work through your model tuning, if you get stuck or want input reach out to us and the community on Discord.