Creative Commons
Crowdflower
pip install mit-d3m
3.0 MB
from mit_d3m import load_dataset
dataset = load_dataset('first-gop-debate-twitter-sentiment')
X = dataset.X
y = dataset.y
context = dataset.context
0.6710517550914304
mlprimitives.preprocessing.ClassEncoder
mlprimitives.text.TextCleaner
mlprimitives.feature_extraction.StringVectorizer
sklearn.preprocessing.Imputer
xgboost.XGBClassifier
mlprimitives.preprocessing.ClassDecoder
mlprimitives.preprocessing.ClassEncoder#1
mlprimitives.text.TextCleaner#1
"language": multi
"column": texts
"lower": true
"accents": true
"stopwrods": true
"non_alpha": false
"single_chars": true
mlprimitives.feature_extraction.StringVectorizer#1
"features": auto
"input": content
"decode_error": ignore
"analyzer": word
"lowercase": true
"binary": true
"max_features": 68
sklearn.preprocessing.Imputer#1
"missing_values": NaN
"axis": 0
"copy": true
"strategy": mean
xgboost.XGBClassifier#1
"n_jobs": -1
"n_estimators": 939
"max_depth": 9
"learning_rate": 0.019228486299879832
"gamma": 0.64172740150486
"min_child_weight": 8
mlprimitives.preprocessing.ClassDecoder#1
| ColIndex | ColName | ColType | Role |
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