Comments (1)
Cat codes transformation is able to deal with new values while predicting
Could you tell me more about this fabulous "Cat codes" transformation? Which Python class, in which Python package? Perhaps it would be appropriate to incorporate it into JPMML-SkLearn and SkLearn2PMML projects.
Can you suggest a way how to deal with the PMML with the encoding, either in python or in JPMML side.
When dealing with categorical features, then the first transformation of a pipeline should be sklearn2pmml.decoration.CategoricalDomain
, which lets you customize missing value and invalid value treatments according to the PMML specification.
You could try the following parametrizations:
CategoricalDomain(invalid_value_treatment = "as_is")
- Invalid values are dispatched to the model. It is assumed that the model contains logic for detecting and handling these.CategoricalDomain(invalid_value_treatment = "as_missing")
- Invalid values are converted to missing values, and then dispatched to the model.CategoricalDomain(invalid_value_treatment = "as_missing", missing_value_replacement = "1.0")
. Convert invalid values to missing values, and then perform missing value replacement. The total effect is that the model sees invalid values as "1.0".
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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