---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
widget:
structuredData:
attribute_0:
attribute_1:
attribute_2:
attribute_3:
loading:
measurement_0:
measurement_1:
measurement_10:
measurement_11:
measurement_12:
measurement_13:
measurement_14:
measurement_15:
measurement_16:
measurement_17:
measurement_2:
measurement_3:
measurement_4:
measurement_5:
measurement_6:
measurement_7:
measurement_8:
measurement_9:
product_code:
---
# Model description
This is a copy of (tabular-playground)[https://huggingface.co/scikit-learn/tabular-playground] for testing purposes.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|-----------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| memory | |
| steps | [('transformation', ColumnTransformer(transformers=[('loading_missing_value_imputer',
SimpleImputer(), ['loading']),
('numerical_missing_value_imputer',
SimpleImputer(),
['loading', 'measurement_3', 'measurement_4',
'measurement_5', 'measurement_6',
'measurement_7', 'measurement_8',
'measurement_9', 'measurement_10',
'measurement_11', 'measurement_12',
'measurement_13', 'measurement_14',
'measurement_15', 'measurement_16',
'measurement_17']),
('attribute_0_encoder', OneHotEncoder(),
['attribute_0']),
('attribute_1_encoder', OneHotEncoder(),
['attribute_1']),
('product_code_encoder', OneHotEncoder(),
['product_code'])])), ('model', DecisionTreeClassifier(max_depth=4))] |
| verbose | False |
| transformation | ColumnTransformer(transformers=[('loading_missing_value_imputer',
SimpleImputer(), ['loading']),
('numerical_missing_value_imputer',
SimpleImputer(),
['loading', 'measurement_3', 'measurement_4',
'measurement_5', 'measurement_6',
'measurement_7', 'measurement_8',
'measurement_9', 'measurement_10',
'measurement_11', 'measurement_12',
'measurement_13', 'measurement_14',
'measurement_15', 'measurement_16',
'measurement_17']),
('attribute_0_encoder', OneHotEncoder(),
['attribute_0']),
('attribute_1_encoder', OneHotEncoder(),
['attribute_1']),
('product_code_encoder', OneHotEncoder(),
['product_code'])]) |
| model | DecisionTreeClassifier(max_depth=4) |
| transformation__n_jobs | |
| transformation__remainder | drop |
| transformation__sparse_threshold | 0.3 |
| transformation__transformer_weights | |
| transformation__transformers | [('loading_missing_value_imputer', SimpleImputer(), ['loading']), ('numerical_missing_value_imputer', SimpleImputer(), ['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']), ('attribute_0_encoder', OneHotEncoder(), ['attribute_0']), ('attribute_1_encoder', OneHotEncoder(), ['attribute_1']), ('product_code_encoder', OneHotEncoder(), ['product_code'])] |
| transformation__verbose | False |
| transformation__verbose_feature_names_out | True |
| transformation__loading_missing_value_imputer | SimpleImputer() |
| transformation__numerical_missing_value_imputer | SimpleImputer() |
| transformation__attribute_0_encoder | OneHotEncoder() |
| transformation__attribute_1_encoder | OneHotEncoder() |
| transformation__product_code_encoder | OneHotEncoder() |
| transformation__loading_missing_value_imputer__add_indicator | False |
| transformation__loading_missing_value_imputer__copy | True |
| transformation__loading_missing_value_imputer__fill_value | |
| transformation__loading_missing_value_imputer__missing_values | nan |
| transformation__loading_missing_value_imputer__strategy | mean |
| transformation__loading_missing_value_imputer__verbose | 0 |
| transformation__numerical_missing_value_imputer__add_indicator | False |
| transformation__numerical_missing_value_imputer__copy | True |
| transformation__numerical_missing_value_imputer__fill_value | |
| transformation__numerical_missing_value_imputer__missing_values | nan |
| transformation__numerical_missing_value_imputer__strategy | mean |
| transformation__numerical_missing_value_imputer__verbose | 0 |
| transformation__attribute_0_encoder__categories | auto |
| transformation__attribute_0_encoder__drop | |
| transformation__attribute_0_encoder__dtype |
Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(),['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3','measurement_4','measurement_5','measurement_6','measurement_7','measurement_8','measurement_9','measurement_10','measurement_11','measurement_12','measurement_13','measurement_14','measurement_15','measurement_16','measurement_17']),('attribute_0_encoder',OneHotEncoder(),['attribute_0']),('attribute_1_encoder',OneHotEncoder(),['attribute_1']),('product_code_encoder',OneHotEncoder(),['product_code'])])),('model', DecisionTreeClassifier(max_depth=4))])Please rerun this cell to show the HTML repr or trust the notebook.
Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(),['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3','measurement_4','measurement_5','measurement_6','measurement_7','measurement_8','measurement_9','measurement_10','measurement_11','measurement_12','measurement_13','measurement_14','measurement_15','measurement_16','measurement_17']),('attribute_0_encoder',OneHotEncoder(),['attribute_0']),('attribute_1_encoder',OneHotEncoder(),['attribute_1']),('product_code_encoder',OneHotEncoder(),['product_code'])])),('model', DecisionTreeClassifier(max_depth=4))])
ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(), ['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3', 'measurement_4','measurement_5', 'measurement_6','measurement_7', 'measurement_8','measurement_9', 'measurement_10','measurement_11', 'measurement_12','measurement_13', 'measurement_14','measurement_15', 'measurement_16','measurement_17']),('attribute_0_encoder', OneHotEncoder(),['attribute_0']),('attribute_1_encoder', OneHotEncoder(),['attribute_1']),('product_code_encoder', OneHotEncoder(),['product_code'])])
['loading']
SimpleImputer()
['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']
SimpleImputer()
['attribute_0']
OneHotEncoder()
['attribute_1']
OneHotEncoder()
['product_code']
OneHotEncoder()
DecisionTreeClassifier(max_depth=4)