metadata

license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_format: pickle
model_file: catboost_without_hospital_number.pkl
widget:
  structuredData:
    abdomen:
      - distend_small
      - distend_small
      - distend_large
    abdominal_distention:
      - none
      - none
      - moderate
    abdomo_appearance:
      - serosanguious
      - cloudy
      - serosanguious
    abdomo_protein:
      - 4.1
      - 4.3
      - 2
    age:
      - adult
      - adult
      - adult
    capillary_refill_time:
      - less_3_sec
      - less_3_sec
      - more_3_sec
    cp_data:
      - 'yes'
      - 'yes'
      - 'no'
    lesion_1:
      - 7209
      - 2112
      - 5400
    lesion_2:
      - 0
      - 0
      - 0
    lesion_3:
      - 0
      - 0
      - 0
    mucous_membrane:
      - bright_pink
      - bright_pink
      - dark_cyanotic
    nasogastric_reflux:
      - none
      - none
      - more_1_liter
    nasogastric_reflux_ph:
      - 7
      - 3.5
      - 2
    nasogastric_tube:
      - slight
      - none
      - significant
    packed_cell_volume:
      - 37
      - 44
      - 65
    pain:
      - depressed
      - mild_pain
      - extreme_pain
    peripheral_pulse:
      - normal
      - normal
      - reduced
    peristalsis:
      - hypermotile
      - hypomotile
      - absent
    pulse:
      - 84
      - 66
      - 72
    rectal_exam_feces:
      - absent
      - decreased
      - absent
    rectal_temp:
      - 39
      - 38.5
      - 37.3
    respiratory_rate:
      - 24
      - 21
      - 30
    surgery:
      - 'yes'
      - 'yes'
      - 'yes'
    surgical_lesion:
      - 'yes'
      - 'yes'
      - 'yes'
    temp_of_extremities:
      - cool
      - normal
      - cool
    total_protein:
      - 6.5
      - 7.6
      - 13

Model description

This is a Catboost model trained on horse health outcome data from Kaggle.

Intended uses & limitations

This model is not ready to be used in production.

Training Procedure

[More Information Needed]

Hyperparameters

Click to expand

Hyperparameter	Value
memory
steps	[('preprocessor', ColumnTransformer(remainder='passthrough', transformers=[('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), ['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']), ('cat', Pipeline(steps=[('imputer', SimpleI...='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data'])])), ('classifier', <catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10>)]
verbose	False
preprocessor	ColumnTransformer(remainder='passthrough', transformers=[('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), ['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']), ('cat', Pipeline(steps=[('imputer', SimpleI...='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data'])])
classifier	<catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10>
preprocessor__n_jobs
preprocessor__remainder	passthrough
preprocessor__sparse_threshold	0.3
preprocessor__transformer_weights
preprocessor__transformers	[('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), ['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']), ('cat', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data'])]
preprocessor__verbose	False
preprocessor__verbose_feature_names_out	True
preprocessor__num	Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])
preprocessor__cat	Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor__num__memory
preprocessor__num__steps	[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]
preprocessor__num__verbose	False
preprocessor__num__imputer	SimpleImputer(strategy='median')
preprocessor__num__scaler	StandardScaler()
preprocessor__num__imputer__add_indicator	False
preprocessor__num__imputer__copy	True
preprocessor__num__imputer__fill_value
preprocessor__num__imputer__keep_empty_features	False
preprocessor__num__imputer__missing_values	nan
preprocessor__num__imputer__strategy	median
preprocessor__num__scaler__copy	True
preprocessor__num__scaler__with_mean	True
preprocessor__num__scaler__with_std	True
preprocessor__cat__memory
preprocessor__cat__steps	[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))]
preprocessor__cat__verbose	False
preprocessor__cat__imputer	SimpleImputer(fill_value='missing', strategy='constant')
preprocessor__cat__onehot	OneHotEncoder(handle_unknown='ignore')
preprocessor__cat__imputer__add_indicator	False
preprocessor__cat__imputer__copy	True
preprocessor__cat__imputer__fill_value	missing
preprocessor__cat__imputer__keep_empty_features	False
preprocessor__cat__imputer__missing_values	nan
preprocessor__cat__imputer__strategy	constant
preprocessor__cat__onehot__categories	auto
preprocessor__cat__onehot__drop
preprocessor__cat__onehot__dtype	<class 'numpy.float64'>
preprocessor__cat__onehot__feature_name_combiner	concat
preprocessor__cat__onehot__handle_unknown	ignore
preprocessor__cat__onehot__max_categories
preprocessor__cat__onehot__min_frequency
preprocessor__cat__onehot__sparse	deprecated
preprocessor__cat__onehot__sparse_output	True
classifier__learning_rate	0.1
classifier__silent	True
classifier__max_depth	4
classifier__n_estimators	200

Model Plot

Evaluation Results

Metric	Value
accuracy	0.744939
f1 score	0.744939

Confusion Matrix

How to Get Started with the Model

[More Information Needed]

Model Card Authors

kmposkid

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