from mit_d3m import load_dataset
dataset = load_dataset('LL0_197_cpu_act')
X = dataset.X
y = dataset.y
context = dataset.context
5.4338153434263
mlprimitives.feature_extraction.CategoricalEncoder
sklearn.preprocessing.Imputer
sklearn.preprocessing.StandardScaler
xgboost.XGBRegressor
mlprimitives.feature_extraction.CategoricalEncoder#1
"copy": true
"features": auto
"max_labels": 50
sklearn.preprocessing.Imputer#1
"missing_values": NaN
"axis": 0
"copy": true
"strategy": most_frequent
sklearn.preprocessing.StandardScaler#1
"with_mean": true
"with_std": true
xgboost.XGBRegressor#1
"n_jobs": -1
"n_estimators": 895
"max_depth": 3
"learning_rate": 0.10001767453956212
"gamma": 0.5130686330232757
"min_child_weight": 1
| ColIndex | ColName | ColType | Role |
|---|