mldb-6.py

rez = mldb.post("/v1/procedures", {
    "type": "classifier.experiment",
    "params": {
        "experimentName": "car_brand_cls",
        "inputData": """        
            SELECT 
                {* EXCLUDING(brand)} as features,
                brand as label
            FROM training_dataset
        """,
        "mode": "categorical",
        "modelFileUrlPattern": "file:///mldb_data/car_brand_cls.cls",
         "configuration": {
            "type": "bagging",
            "weak_learner": {
                "type": "boosting",
                "weak_learner": {
                    "type": "decision_tree",
                    "max_depth": 5,
                    "update_alg": "gentle",
                    "random_feature_propn": 0.6
                },
                "min_iter": 5,
                "max_iter": 30
            },
            "num_bags": 15
        }
    }
})

runResults = rez.json()["status"]["firstRun"]["status"]["folds"][0]["resultsTest"]
print rez