File size: 32,212 Bytes
c3bc089 50b8c47 c3bc089 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 |
---
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
- 2.0
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:
- 7.0
- 3.5
- 2.0
nasogastric_tube:
- slight
- none
- significant
packed_cell_volume:
- 37.0
- 44.0
- 65.0
pain:
- depressed
- mild_pain
- extreme_pain
peripheral_pulse:
- normal
- normal
- reduced
peristalsis:
- hypermotile
- hypomotile
- absent
pulse:
- 84.0
- 66.0
- 72.0
rectal_exam_feces:
- absent
- decreased
- absent
rectal_temp:
- 39.0
- 38.5
- 37.3
respiratory_rate:
- 24.0
- 21.0
- 30.0
surgery:
- 'yes'
- 'yes'
- 'yes'
surgical_lesion:
- 'yes'
- 'yes'
- 'yes'
temp_of_extremities:
- cool
- normal
- cool
total_protein:
- 6.5
- 7.6
- 13.0
---
# 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
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|
| memory | |
| steps | [('preprocessor', ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])])), ('classifier', LGBMClassifier(max_depth=3))] |
| verbose | False |
| preprocessor | ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> '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')),<br /> ('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',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('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')),<br /> ('scaler', StandardScaler())]) |
| preprocessor__cat | Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('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 |
</details>
### Model Plot
<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>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))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>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'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['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']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-30" type="checkbox" ><label for="sk-estimator-id-30" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-31" type="checkbox" ><label for="sk-estimator-id-31" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-32" type="checkbox" ><label for="sk-estimator-id-32" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-33" type="checkbox" ><label for="sk-estimator-id-33" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMClassifier</label><div class="sk-toggleable__content"><pre>LGBMClassifier(max_depth=3)</pre></div></div></div></div></div></div></div>
## Evaluation Results
| Metric | Value |
|----------|----------|
| accuracy | 0.740891 |
| f1 score | 0.740891 |
### Confusion Matrix
![Confusion Matrix](confusion_matrix.png)
## Permutation Importance
![Permutation Importance](feature_importance.png)
# 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
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
|