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 | <class 'numpy.float64'> | | transformation__attribute_0_encoder__handle_unknown | error | | transformation__attribute_0_encoder__sparse | True | | transformation__attribute_1_encoder__categories | auto | | transformation__attribute_1_encoder__drop | | | transformation__attribute_1_encoder__dtype | <class 'numpy.float64'> | | transformation__attribute_1_encoder__handle_unknown | error | | transformation__attribute_1_encoder__sparse | True | | transformation__product_code_encoder__categories | auto | | transformation__product_code_encoder__drop | | | transformation__product_code_encoder__dtype | <class 'numpy.float64'> | | transformation__product_code_encoder__handle_unknown | error | | transformation__product_code_encoder__sparse | True | | model__ccp_alpha | 0.0 | | model__class_weight | | | model__criterion | gini | | model__max_depth | 4 | | model__max_features | | | model__max_leaf_nodes | | | model__min_impurity_decrease | 0.0 | | model__min_samples_leaf | 1 | | model__min_samples_split | 2 | | model__min_weight_fraction_leaf | 0.0 | | model__random_state | | | model__splitter | best |
Model Plot
The model plot is below.
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)
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|---|---|
| accuracy | 0.7888 |
| f1 score | 0.7888 |
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
import pickle
with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file:
clf = pickle.load(file)
Model Card Authors
This model card is written by following authors:
huggingface
Model Card Contact
You can contact the model card authors through following channels: [More Information Needed]
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Tree Plot
Confusion Matrix
