Getting Fit Reports#
Supervised fits are done, we display details about the fit process through
In this page, we’ll take a look on the information that is available.
There’s 2 levels of verbose that set the amount of information that is retrieved.
The default value for default on the setup/fit method is
verbose=1, while using the
the default value is
verbose=0 will just not return any information.
Some information is only displayed when accessing the dictionary returned on
Here are the information that is available with the reporting system:
verbose = 1#
Always printed when report method is used.
Loading from checkpoint: Specifies which epoch the model had the best performance and is used as the final model for inference.
Model Evaluation [Supervised only]: Metrics on the test set.
MSE and MAE for regression,
'quantile'=0.5is used when
Scikit-learn’s classification_report (precision, recall and f1) when
verbose = 2#
Contains all content of
Model Training: Plots the loss graph of the training. When
return_report=True, returns the values of the loss for the training set and validation set for each epoch.
All content below is available only when
Auto lr finder: If
learning_rate=0, then we’ll try and find the best appropriate learning rate.
All content below is available only on Supervised models.
Metrics Train/Metrics Validation: The same metrics calculated on Model Evaluation but on the training set and validation set respectively.
Baseline Model: The same metrics of “Model Evaluation” on the test set, but using a Baseline Model (please check sklearn.dummy models). For regression cases, we evaluate the mean and the median as a baseline. For classification cases, we use
most_frequentmodels as baseline.
Optimal Thresholds: List the probability thresholds that maximize
true positive rate - false negative ratefor each class. Since it’s calculated in a OneVsAll manner, the probabilities don’t sum up to one.