from mit_d3m import load_dataset
dataset = load_dataset('LL0_1464_blood_transfusion_service_center')
X = dataset.X
y = dataset.y
context = dataset.context
0.6705744228880268
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": 95
sklearn.preprocessing.Imputer#1
"missing_values": NaN
"axis": 0
"copy": true
"strategy": median
sklearn.preprocessing.StandardScaler#1
"with_mean": false
"with_std": true
mlprimitives.preprocessing.ClassEncoder#1
xgboost.XGBClassifier#1
"n_jobs": -1
"n_estimators": 709
"max_depth": 4
"learning_rate": 0.8676838347896283
"gamma": 0.9751334069760365
"min_child_weight": 5
mlprimitives.preprocessing.ClassDecoder#1
| ColIndex | ColName | ColType | Role |
|---|