Machine Learning

Breeze allows for different applications of machine learning algorithms.

Training

Description

Training predictions models using Machine Learning from different frameworks. And using ONNX Runtime as inference engine.

Options

Algorithm

If Auto is selected the system will iterate over different data featurizations, machine learning algorithms, and hyperparameters to select the best model. Otherwise the specified algorithm will be used.

Time

Specify the maximum time that the experiment is allowed to run. Any started experiment will finish even if it exceeds the specified time limit.

Number of cross validation folds

Number of partitions created for cross validation experiment (see below).

Cross validate all experiments

Cross-validation is a training and model evaluation technique that splits the data into several partitions and trains multiple algorithms on these partitions. This technique improves the robustness of the model by holding out data from the training process. In addition to improving performance on unseen observations, in data-constrained environments it can be an effective tool for training models with a smaller dataset.

Optimization metrics

Classification

Macro-Accuracy is the average accuracy at class level. The accuracy for each class is computed and the macro-accuracy is the average of these accuracies. The closer to 1.00, the better. See Classification (ML training)

Quantification

Gets the R-squared value of the model, which is also known as the coefficient of determination. R-Squared closer to 1 indicates a better fitted model. See Quantification (ML training)