bigmler --train ../data/grades.csv --test ../data/test_grades.csv --output ./scenario1_r/predictions.csv --max-batch-models 1
bigmler --train ../data/iris.csv --test ../data/test_iris.csv --output ./scenario1/predictions.csv --max-batch-models 1
bigmler --source source/5186f67a925ded084a000096 --test ../data/test_iris.csv --output ./scenario2/predictions.csv
bigmler --dataset dataset/5186f690925ded084a00009d --test ../data/test_iris.csv --output ./scenario3/predictions.csv
bigmler --model model/5186f6a2925ded0846000083 --test ../data/test_iris.csv --output ./scenario4/predictions.csv --max-batch-models 1
bigmler --dataset dataset/5186f690925ded084a00009d --test ../data/test_iris.csv --number-of-models 10 --tag my_ensemble --output ./scenario5/predictions.csv
bigmler --models ./scenario5/models --test ../data/test_iris.csv --output ./scenario6/predictions.csv
bigmler --datasets ./scenario1/dataset --test ../data/test_iris.csv --output ./scenario7/predictions.csv
bigmler --combine-votes ./scenario1,./scenario5 --output ./scenario8/predictions.csv
bigmler --combine-votes ./scenario1,./scenario5 --output ./scenario9/predictions_c.csv --method "confidence weighted"
bigmler --combine-votes ./scenario1,./scenario5 --output ./scenario9/predictions_p.csv --method "probability weighted"
bigmler --combine-votes ./scenario1_r,./scenario1_r --output ./scenario10/predictions_c.csv --method "confidence weighted"
bigmler --combine-votes ./scenario1_r,./scenario1_r --output ./scenario10/predictions_p.csv --method "probability weighted"
bigmler --dataset dataset/5186f690925ded084a00009d --objective 0 --model-fields "petal length,petal width" --test ../data/test_iris.csv --output ./scenario11/predictions.csv
bigmler --dataset dataset/5186f690925ded084a00009d --cross-validation-rate 0.02 --output ./scenario12/cross-validation
bigmler --train ../data/iris.csv --locale es_ES.UTF-8 --output ./scenario13/store_file --no-dataset --no-model --store
bigmler --evaluate --train ../data/iris.csv --output ./scenario_e1/evaluation
bigmler --evaluate --source source/5186f786925ded084a0000bf --output ./scenario_e2/evaluation
bigmler --evaluate --dataset dataset/5186f798925ded0848000094 --output ./scenario_e3/evaluation
bigmler --evaluate --test ../data/iris.csv --model model/5186f7a4925ded084800009c --output ./scenario_e4/evaluation
bigmler --evaluate --dataset dataset/5186f7ae925ded084a0000d4 --model model/5186f7a4925ded084800009c --output ./scenario_e5/evaluation
bigmler --evaluate --train ../data/iris.csv --test-split 0.2 --output ./scenario_e6/evaluation
