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Horse-Health-Outcome-6d56348a-8c02-416a-8d5d-fdf98b0d4f59

Tabular Classification
Scikit-learn
skops
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Horse-Health-Outcome-6d56348a-8c02-416a-8d5d-fdf98b0d4f59 / README.md
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metadata
license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_format: pickle
model_file: LightGBM_without_hospital_number_01.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 LightGBM 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', LGBMClassifier(max_depth=3))]
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 LGBMClassifier(max_depth=3)
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__boosting_type gbdt
classifier__class_weight
classifier__colsample_bytree 1.0
classifier__importance_type split
classifier__learning_rate 0.1
classifier__max_depth 3
classifier__min_child_samples 20
classifier__min_child_weight 0.001
classifier__min_split_gain 0.0
classifier__n_estimators 100
classifier__n_jobs
classifier__num_leaves 31
classifier__objective
classifier__random_state
classifier__reg_alpha 0.0
classifier__reg_lambda 0.0
classifier__subsample 1.0
classifier__subsample_for_bin 200000
classifier__subsample_freq 0

Model Plot 

Pipeline(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',Pi...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', LGBMClassifier(max_depth=3))])
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Evaluation Results

Metric Value
accuracy 0.740891
f1 score 0.740891

Confusion Matrix

Confusion Matrix

How to Get Started with the Model

[More Information Needed]

Model Card Authors

kmposkid

Model Card Contact

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

Citation

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