Model training

Training with GenoML evaluates several different algorithms and outputs the best algorithm based on a specific metric that can be tweaked using the --metric_max flag (default is AUC).

Required arguments for GenoML are the following:

• data : Is the data continuous or discrete?
• method: Do you want to use supervised or unsupervised machine learning? (unsupervised currently under development)
• mode: would you like to munge, train, tune, or test your model?
• --prefix : Where would you like your outputs to be saved?

The most basic command to train your model looks like the following; it looks for the *.dataForML file that was generated in the munging step:

# Running GenoML supervised training after munging on discrete data

genoml discrete supervised train \

--prefix outputs/test_discrete_geno

If you would like to determine the best competing algorithm by something other than the AUC, you can do so by changing the --metric_max flag (Options include AUC, Balanced_Accuracy, Sensitivity, and Specificity) :

# Running GenoML supervised training after munging on discrete data and specifying the metric to maximize by

genoml discrete supervised train \

--prefix outputs/test_discrete_geno \

--metric_max Sensitivity

caution

The --metric_max flag is only available for discrete datasets.