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
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/51914b1c925ded7ea60006fd --test ../data/test_iris.csv --output ./scenario2/predictions.csv
bigmler --dataset dataset/51914b34925ded7ea70009d3 --test ../data/test_iris.csv --output ./scenario3/predictions.csv
bigmler --model model/51914b49925ded7ea40005dd --test ../data/test_iris.csv --output ./scenario4/predictions.csv --max-batch-models 1
bigmler --dataset dataset/51914b34925ded7ea70009d3 --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/51914b34925ded7ea70009d3 --objective 0 --model-fields "petal length,petal width" --test ../data/test_iris.csv --output ./scenario11/predictions.csv
bigmler --dataset dataset/51914b34925ded7ea70009d3 --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/51914c17925ded7ea5000523 --output ./scenario_e2/evaluation
bigmler --evaluate --dataset dataset/51914c2b925ded7ea70009ff --output ./scenario_e3/evaluation
bigmler --evaluate --test ../data/iris.csv --model model/51914c3a925ded7ea600071d --output ./scenario_e4/evaluation
bigmler --evaluate --dataset dataset/51914c46925ded7ea7000a0f --model model/51914c3a925ded7ea600071d --output ./scenario_e5/evaluation
bigmler --evaluate --train ../data/iris.csv --test-split 0.2 --output ./scenario_e6/evaluation
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/51926561925ded7ea6000a63 --test ../data/test_iris.csv --output ./scenario2/predictions.csv
bigmler --dataset dataset/5192657b925ded7ea3000753 --test ../data/test_iris.csv --output ./scenario3/predictions.csv
bigmler --model model/51926590925ded7ea7000f08 --test ../data/test_iris.csv --output ./scenario4/predictions.csv --max-batch-models 1
bigmler --dataset dataset/5192657b925ded7ea3000753 --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/5192657b925ded7ea3000753 --objective 0 --model-fields "petal length,petal width" --test ../data/test_iris.csv --output ./scenario11/predictions.csv
bigmler --dataset dataset/5192657b925ded7ea3000753 --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/5192665d925ded7ea6000a87 --output ./scenario_e2/evaluation
bigmler --evaluate --dataset dataset/5192666f925ded7ea4000852 --output ./scenario_e3/evaluation
bigmler --evaluate --test ../data/iris.csv --model model/51926685925ded7ea50007a5 --output ./scenario_e4/evaluation
bigmler --evaluate --dataset dataset/51926692925ded7ea400085a --model model/51926685925ded7ea50007a5 --output ./scenario_e5/evaluation
bigmler --evaluate --train ../data/iris.csv --test-split 0.2 --output ./scenario_e6/evaluation
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/51d4c0d3035d076036000f0a --test ../data/test_iris.csv --output ./scenario2/predictions.csv
bigmler --dataset dataset/51d4c0eb035d07603b000c17 --test ../data/test_iris.csv --output ./scenario3/predictions.csv
bigmler --model model/51d4c0ff035d076039001f1c --test ../data/test_iris.csv --output ./scenario4/predictions.csv --max-batch-models 1
bigmler --dataset dataset/51d4c0eb035d07603b000c17 --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/51d4c0eb035d07603b000c17 --objective 0 --model-fields "petal length,petal width" --test ../data/test_iris.csv --output ./scenario11/predictions.csv
bigmler --dataset dataset/51d4c0eb035d07603b000c17 --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/51d4c1e1035d07603c000f22 --output ./scenario_e2/evaluation
bigmler --evaluate --dataset dataset/51d4c1f8035d07603c000f25 --output ./scenario_e3/evaluation
bigmler --evaluate --test ../data/iris.csv --model model/51d4c208035d07603b000c21 --output ./scenario_e4/evaluation
bigmler --evaluate --dataset dataset/51d4c214035d076039001f50 --model model/51d4c208035d07603b000c21 --output ./scenario_e5/evaluation
bigmler --evaluate --train ../data/iris.csv --test-split 0.2 --output ./scenario_e6/evaluation
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/51d4c875035d07603800146d --test ../data/test_iris.csv --output ./scenario2/predictions.csv
bigmler --dataset dataset/51d4c88f035d076038001470 --test ../data/test_iris.csv --output ./scenario3/predictions.csv
bigmler --model model/51d4c8a3035d07603c000f73 --test ../data/test_iris.csv --output ./scenario4/predictions.csv --max-batch-models 1
bigmler --dataset dataset/51d4c88f035d076038001470 --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/51d4c88f035d076038001470 --objective 0 --model-fields "petal length,petal width" --test ../data/test_iris.csv --output ./scenario11/predictions.csv
bigmler --dataset dataset/51d4c88f035d076038001470 --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/51d4c968035d076039002053 --output ./scenario_e2/evaluation
bigmler --evaluate --dataset dataset/51d4c97e035d07603900205c --output ./scenario_e3/evaluation
bigmler --evaluate --test ../data/iris.csv --model model/51d4c98e035d076036000f86 --output ./scenario_e4/evaluation
bigmler --evaluate --dataset dataset/51d4c99c035d07603800148d --model model/51d4c98e035d076036000f86 --output ./scenario_e5/evaluation
bigmler --evaluate --train ../data/iris.csv --test-split 0.2 --output ./scenario_e6/evaluation
