Build the categorization ML model

The option use_hashing=True must be set for the feature extraction. Recommended options also include, use_idf=1, sublinear_tf=0, binary=0.

• URL: /api/v0/categorization/

• Method: POST URL Params: None

• Data Params:

  • dataset_id: dataset id
  • index: [required] document indices of the training set
  • y: [required] target binary class relative to index
  • method: classification algorithm to use (default: LogisticRegression), * “LogisticRegression”: LogisticRegression * “LinearSVC”: Linear SVM, * “xgboost”: Gradient Boosting (Warning: for the moment xgboost is not istalled for a direct install on Windows)
  • cv: binary, if true optimal parameters of the ML model are determined by cross-validation over 5 stratified K-folds (default True).
  • training_scores: binary, compute the efficiency scores on the training dataset (default True).

• Success Response: HTTP 200

   {"id": <str>, "recall": <float>, "precision": <float>,
    "f1": <float>, "roc_auc": <float>, "average_precision": <float>}