from mit_d3m import load_dataset
dataset = load_dataset('LL0_1507_twonorm')
X = dataset.X
y = dataset.y
context = dataset.context
0.9741586429784028
mlprimitives.feature_extraction.CategoricalEncoder
sklearn.preprocessing.Imputer
sklearn.preprocessing.StandardScaler
mlprimitives.preprocessing.ClassEncoder
xgboost.XGBClassifier
mlprimitives.preprocessing.ClassDecoder
mlprimitives.feature_extraction.CategoricalEncoder#1
"copy": true
"features": auto
"max_labels": 50
sklearn.preprocessing.Imputer#1
"missing_values": NaN
"axis": 0
"copy": true
"strategy": mean
sklearn.preprocessing.StandardScaler#1
"with_mean": false
"with_std": true
mlprimitives.preprocessing.ClassEncoder#1
xgboost.XGBClassifier#1
"n_jobs": -1
"n_estimators": 363
"max_depth": 4
"learning_rate": 0.18829597651898722
"gamma": 0.14524160577946799
"min_child_weight": 2
mlprimitives.preprocessing.ClassDecoder#1
| ColIndex | ColName | ColType | Role |
|---|