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merve
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xgboost-example

Tabular Regression
Scikit-learn
skops
Model card Files
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  1. README.md +236 -0
  2. config.json +208 -0
  3. model.pkl +3 -0
README.md ADDED
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+ ---
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+ library_name: sklearn
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-regression
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+ model_format: pickle
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+ model_file: model.pkl
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+ widget:
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+ structuredData:
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+ Fedu:
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+ - 3
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+ - 3
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+ - 3
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+ Fjob:
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+ - other
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+ - other
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+ - services
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+ G1:
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+ - 12
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+ - 13
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+ - 8
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+ G2:
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+ - 13
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+ - 14
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+ - 7
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+ G3:
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+ - 12
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+ - 14
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+ - 0
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+ Medu:
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+ - 3
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+ - 2
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+ - 1
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+ Mjob:
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+ - services
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+ - other
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+ - at_home
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+ Pstatus:
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+ - T
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+ - T
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+ - T
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+ Walc:
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+ - 2
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+ - 1
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+ - 1
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+ absences:
48
+ - 2
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+ - 0
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+ - 0
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+ activities:
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+ - 'yes'
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+ - 'no'
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+ - 'yes'
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+ address:
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+ - U
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+ - U
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+ - U
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+ age:
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+ - 16
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+ - 16
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+ - 16
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+ failures:
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+ - 0
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+ - 0
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+ - 3
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+ famrel:
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+ - 4
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+ - 5
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+ - 4
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+ famsize:
72
+ - GT3
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+ - GT3
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+ - GT3
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+ famsup:
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+ - 'no'
77
+ - 'no'
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+ - 'no'
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+ freetime:
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+ - 2
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+ - 3
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+ - 3
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+ goout:
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+ - 3
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+ - 3
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+ - 5
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+ guardian:
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+ - mother
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+ - father
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+ - mother
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+ health:
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+ - 3
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+ - 3
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+ - 3
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+ higher:
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+ - 'yes'
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+ - 'yes'
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+ - 'yes'
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+ internet:
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+ - 'yes'
101
+ - 'yes'
102
+ - 'yes'
103
+ nursery:
104
+ - 'yes'
105
+ - 'yes'
106
+ - 'no'
107
+ paid:
108
+ - 'yes'
109
+ - 'no'
110
+ - 'no'
111
+ reason:
112
+ - home
113
+ - home
114
+ - home
115
+ romantic:
116
+ - 'yes'
117
+ - 'no'
118
+ - 'yes'
119
+ school:
120
+ - GP
121
+ - GP
122
+ - GP
123
+ schoolsup:
124
+ - 'no'
125
+ - 'no'
126
+ - 'no'
127
+ sex:
128
+ - M
129
+ - M
130
+ - F
131
+ studytime:
132
+ - 2
133
+ - 1
134
+ - 2
135
+ traveltime:
136
+ - 1
137
+ - 2
138
+ - 1
139
+ ---
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+
141
+ # Model description
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+
143
+ [More Information Needed]
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+
145
+ ## Intended uses & limitations
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+
147
+ [More Information Needed]
148
+
149
+ ## Training Procedure
150
+
151
+ ### Hyperparameters
152
+
153
+ The model is trained with below hyperparameters.
154
+
155
+ <details>
156
+ <summary> Click to expand </summary>
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+
158
+ | Hyperparameter | Value |
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+ |---------------------------------------|------------------------------------------------------|
160
+ | memory | |
161
+ | steps | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,<br /> colsample_bynode=1, colsample_bytree=1, enable_categorical=False,<br /> gamma=0, gpu_id=-1, importance_type=None,<br /> interaction_constraints='', learning_rate=0.300000012,<br /> max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,<br /> monotone_constraints='()', n_estimators=100, n_jobs=8,<br /> num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,<br /> reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',<br /> validate_parameters=1, verbosity=None))] |
162
+ | verbose | False |
163
+ | onehotencoder | OneHotEncoder(handle_unknown='ignore', sparse=False) |
164
+ | xgbregressor | XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,<br /> colsample_bynode=1, colsample_bytree=1, enable_categorical=False,<br /> gamma=0, gpu_id=-1, importance_type=None,<br /> interaction_constraints='', learning_rate=0.300000012,<br /> max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,<br /> monotone_constraints='()', n_estimators=100, n_jobs=8,<br /> num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,<br /> reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',<br /> validate_parameters=1, verbosity=None) |
165
+ | onehotencoder__categories | auto |
166
+ | onehotencoder__drop | |
167
+ | onehotencoder__dtype | <class 'numpy.float64'> |
168
+ | onehotencoder__handle_unknown | ignore |
169
+ | onehotencoder__max_categories | |
170
+ | onehotencoder__min_frequency | |
171
+ | onehotencoder__sparse | False |
172
+ | xgbregressor__objective | reg:squarederror |
173
+ | xgbregressor__base_score | 0.5 |
174
+ | xgbregressor__booster | gbtree |
175
+ | xgbregressor__colsample_bylevel | 1 |
176
+ | xgbregressor__colsample_bynode | 1 |
177
+ | xgbregressor__colsample_bytree | 1 |
178
+ | xgbregressor__enable_categorical | False |
179
+ | xgbregressor__gamma | 0 |
180
+ | xgbregressor__gpu_id | -1 |
181
+ | xgbregressor__importance_type | |
182
+ | xgbregressor__interaction_constraints | |
183
+ | xgbregressor__learning_rate | 0.300000012 |
184
+ | xgbregressor__max_delta_step | 0 |
185
+ | xgbregressor__max_depth | 5 |
186
+ | xgbregressor__min_child_weight | 1 |
187
+ | xgbregressor__missing | nan |
188
+ | xgbregressor__monotone_constraints | () |
189
+ | xgbregressor__n_estimators | 100 |
190
+ | xgbregressor__n_jobs | 8 |
191
+ | xgbregressor__num_parallel_tree | 1 |
192
+ | xgbregressor__predictor | auto |
193
+ | xgbregressor__random_state | 0 |
194
+ | xgbregressor__reg_alpha | 0 |
195
+ | xgbregressor__reg_lambda | 1 |
196
+ | xgbregressor__scale_pos_weight | 1 |
197
+ | xgbregressor__subsample | 1 |
198
+ | xgbregressor__tree_method | exact |
199
+ | xgbregressor__validate_parameters | 1 |
200
+ | xgbregressor__verbosity | |
201
+
202
+ </details>
203
+
204
+ ### Model Plot
205
+
206
+ The model plot is below.
207
+
208
+ <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)),(&#x27;xgbregressor&#x27;,XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,colsample_bylevel=1, colsample_bynode=1,colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;,learning_rate=0.300000012, max_delta_step=0,max_depth=5, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100,n_jobs=8, num_parallel_tree=1, predictor=&#x27;auto&#x27;,random_state=0, reg_alpha=0, reg_lambda=1,scale_pos_weight=1, subsample=1,tree_method=&#x27;exact&#x27;, validate_parameters=1,verbosity=None))])</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-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)),(&#x27;xgbregressor&#x27;,XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,colsample_bylevel=1, colsample_bynode=1,colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;,learning_rate=0.300000012, max_delta_step=0,max_depth=5, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100,n_jobs=8, num_parallel_tree=1, predictor=&#x27;auto&#x27;,random_state=0, reg_alpha=0, reg_lambda=1,scale_pos_weight=1, subsample=1,tree_method=&#x27;exact&#x27;, validate_parameters=1,verbosity=None))])</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-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</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-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">XGBRegressor</label><div class="sk-toggleable__content"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method=&#x27;exact&#x27;,validate_parameters=1, verbosity=None)</pre></div></div></div></div></div></div></div>
209
+
210
+ ## Evaluation Results
211
+
212
+ [More Information Needed]
213
+
214
+ # How to Get Started with the Model
215
+
216
+ [More Information Needed]
217
+
218
+ # Model Card Authors
219
+
220
+ This model card is written by following authors:
221
+
222
+ [More Information Needed]
223
+
224
+ # Model Card Contact
225
+
226
+ You can contact the model card authors through following channels:
227
+ [More Information Needed]
228
+
229
+ # Citation
230
+
231
+ Below you can find information related to citation.
232
+
233
+ **BibTeX:**
234
+ ```
235
+ [More Information Needed]
236
+ ```
config.json ADDED
@@ -0,0 +1,208 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "sklearn": {
3
+ "columns": [
4
+ "school",
5
+ "sex",
6
+ "age",
7
+ "address",
8
+ "famsize",
9
+ "Pstatus",
10
+ "Medu",
11
+ "Fedu",
12
+ "Mjob",
13
+ "Fjob",
14
+ "reason",
15
+ "guardian",
16
+ "traveltime",
17
+ "studytime",
18
+ "failures",
19
+ "schoolsup",
20
+ "famsup",
21
+ "paid",
22
+ "activities",
23
+ "nursery",
24
+ "higher",
25
+ "internet",
26
+ "romantic",
27
+ "famrel",
28
+ "freetime",
29
+ "goout",
30
+ "Walc",
31
+ "health",
32
+ "absences",
33
+ "G1",
34
+ "G2",
35
+ "G3"
36
+ ],
37
+ "environment": [
38
+ "scikit-learn=1.1.2, xgboost=1.5.2"
39
+ ],
40
+ "example_input": {
41
+ "Fedu": [
42
+ 3,
43
+ 3,
44
+ 3
45
+ ],
46
+ "Fjob": [
47
+ "other",
48
+ "other",
49
+ "services"
50
+ ],
51
+ "G1": [
52
+ 12,
53
+ 13,
54
+ 8
55
+ ],
56
+ "G2": [
57
+ 13,
58
+ 14,
59
+ 7
60
+ ],
61
+ "G3": [
62
+ 12,
63
+ 14,
64
+ 0
65
+ ],
66
+ "Medu": [
67
+ 3,
68
+ 2,
69
+ 1
70
+ ],
71
+ "Mjob": [
72
+ "services",
73
+ "other",
74
+ "at_home"
75
+ ],
76
+ "Pstatus": [
77
+ "T",
78
+ "T",
79
+ "T"
80
+ ],
81
+ "Walc": [
82
+ 2,
83
+ 1,
84
+ 1
85
+ ],
86
+ "absences": [
87
+ 2,
88
+ 0,
89
+ 0
90
+ ],
91
+ "activities": [
92
+ "yes",
93
+ "no",
94
+ "yes"
95
+ ],
96
+ "address": [
97
+ "U",
98
+ "U",
99
+ "U"
100
+ ],
101
+ "age": [
102
+ 16,
103
+ 16,
104
+ 16
105
+ ],
106
+ "failures": [
107
+ 0,
108
+ 0,
109
+ 3
110
+ ],
111
+ "famrel": [
112
+ 4,
113
+ 5,
114
+ 4
115
+ ],
116
+ "famsize": [
117
+ "GT3",
118
+ "GT3",
119
+ "GT3"
120
+ ],
121
+ "famsup": [
122
+ "no",
123
+ "no",
124
+ "no"
125
+ ],
126
+ "freetime": [
127
+ 2,
128
+ 3,
129
+ 3
130
+ ],
131
+ "goout": [
132
+ 3,
133
+ 3,
134
+ 5
135
+ ],
136
+ "guardian": [
137
+ "mother",
138
+ "father",
139
+ "mother"
140
+ ],
141
+ "health": [
142
+ 3,
143
+ 3,
144
+ 3
145
+ ],
146
+ "higher": [
147
+ "yes",
148
+ "yes",
149
+ "yes"
150
+ ],
151
+ "internet": [
152
+ "yes",
153
+ "yes",
154
+ "yes"
155
+ ],
156
+ "nursery": [
157
+ "yes",
158
+ "yes",
159
+ "no"
160
+ ],
161
+ "paid": [
162
+ "yes",
163
+ "no",
164
+ "no"
165
+ ],
166
+ "reason": [
167
+ "home",
168
+ "home",
169
+ "home"
170
+ ],
171
+ "romantic": [
172
+ "yes",
173
+ "no",
174
+ "yes"
175
+ ],
176
+ "school": [
177
+ "GP",
178
+ "GP",
179
+ "GP"
180
+ ],
181
+ "schoolsup": [
182
+ "no",
183
+ "no",
184
+ "no"
185
+ ],
186
+ "sex": [
187
+ "M",
188
+ "M",
189
+ "F"
190
+ ],
191
+ "studytime": [
192
+ 2,
193
+ 1,
194
+ 2
195
+ ],
196
+ "traveltime": [
197
+ 1,
198
+ 2,
199
+ 1
200
+ ]
201
+ },
202
+ "model": {
203
+ "file": "model.pkl"
204
+ },
205
+ "model_format": "pickle",
206
+ "task": "tabular-regression"
207
+ }
208
+ }
model.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
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