0xrushi/TestPlaygroundSkops

Model description 1

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Intended uses & limitations

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Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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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))])

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Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value

How to Get Started with the Model

Use the code below to get started with the model.

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Model Card Authors

This model card is written by following authors:

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Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

Below you can find information related to citation.

BibTeX:

    
    # h1
    tjos osmda

    

Model 2 Description (Logistic)

---

license: mit

Model description

[More Information Needed]

Intended uses & limitations

[More Information Needed]

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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Hyperparameter	Value
C	1.0
class_weight
dual	False
fit_intercept	True
intercept_scaling	1
l1_ratio
max_iter	100
multi_class	auto
n_jobs
penalty	l2
random_state	0
solver	liblinear
tol	0.0001
verbose	0
warm_start	False

Model Plot

The model plot is below.

LogisticRegression(random_state=0, solver='liblinear')

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Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value
accuracy	0.96
f1 score	0.96

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Model Card Authors

This model card is written by following authors:

[More Information Needed]

Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

Below you can find information related to citation.

BibTeX:

[More Information Needed]

Additional Content

confusion_matrix