Comments (1)
Duplicate of: jpmml/jpmml-sklearn#41
Python class sklearn.ensemble.AdaBoostClassifier
is not supported, because its predict(X)
and predict_proba(X)
methods yield inconsistent results.
from sklearn2pmml.
Related Issues (20)
- Revisit the missing value handling of custom transformers HOT 1
- Support for AgglomerativeClustering HOT 2
- `ExpressionTransformer` should try to rectify feature type information HOT 5
- `Alias` object has no attribute `transformer_` HOT 8
- Storing LinearSVC as coefficients instead of support vectors HOT 1
- Reshape transformation results from 2-D to 1-D (column vectors) HOT 1
- sklearn2pmml does not work with sklearn version 1.3.0 and newer HOT 6
- sklearn2pmml does not work with xgboost >= 2.0.0 HOT 9
- LookupTransformer TypeError when default value is not exactly the type from the mapping HOT 2
- xgb 生成的pkl 转pmml 失败 HOT 1
- Can sklearn2pmml just for variable transformations be used? HOT 1
- PyPMML is making systematically off predictions with XGBoost PMML documents? HOT 16
- ImportError: cannot import name 'LabelBinarizer' from 'sklearn2pmml.preprocessing' HOT 2
- Compatibility with Scikit-Learn 1.4.0 HOT 8
- Convenience API for embedding model evaluation metrics as `ModelExplanation` element HOT 13
- Ability to "refine" (categorical-) valid value spaces along the transformer pipeline HOT 17
- NullPointerException when using OneHotEncoder with Integer Categorical Variables in sklearn2pmml HOT 10
- Create .pmml File for Calibrated xgboost Model HOT 8
- Pandas 2.2 FutureWarning for time series offset aliases HOT 2
- Pandas not installed as dependency HOT 2
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