---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
widget:
structuredData:
attribute_0:
- material_7
- material_7
- material_7
attribute_1:
- material_6
- material_5
- material_6
attribute_2:
- 6
- 6
- 6
attribute_3:
- 9
- 6
- 9
loading:
- 101.52
- 91.34
- 167.03
measurement_0:
- 9
- 10
- 11
measurement_1:
- 11
- 11
- 5
measurement_10:
- 14.926
- 15.162
- 16.398
measurement_11:
- 20.394
- 19.46
- 20.613
measurement_12:
- 11.829
- 9.114
- 11.007
measurement_13:
- 16.195
- 16.024
- 16.061
measurement_14:
- 16.517
- 17.132
- 15.18
measurement_15:
- 13.826
- 12.257
- 15.758
measurement_16:
- 14.206
- 15.094
- .nan
measurement_17:
- 723.712
- 896.835
- 893.454
measurement_2:
- 2
- 10
- 6
measurement_3:
- 17.492
- 18.114
- 18.42
measurement_4:
- 13.962
- 10.185
- 13.565
measurement_5:
- 15.716
- 18.06
- 16.916
measurement_6:
- 17.104
- 18.283
- 17.917
measurement_7:
- 12.377
- 10.957
- 10.394
measurement_8:
- 19.221
- 20.638
- 19.805
measurement_9:
- 11.613
- 11.804
- 12.012
product_code:
- E
- D
- E
---
# Model description
This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.
## 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)