... commit message ...

README.md

@@ -3,91 +3,49 @@ library_name: sklearn
 tags:
 - sklearn
 - skops
-- tabular-classification
+- tabular-regression
 model_format: pickle
 model_file: model.pkl
 widget:
 - structuredData:
     x0:
-    - -0.09392700711129204
-    - 2.371755361225434
-    - 0.9671947566078468
+    - 187785.0
     x1:
-    - 1.6715939059930913
-    - 1.0187539498438483
-    - 0.31581839557288527
+    - 0.0
     x10:
-    - 1.974051683532626
-    - 0.5529279014966972
-    - 1.4204609635013674
+    - 98.45860046637567
     x11:
-    - 0.2611945709034309
-    - 0.6196937299076192
-    - 1.190400503805864
+    - 61.657549555908474
     x12:
-    - -0.10332312980738237
-    - -1.5650628809571772
-    - -1.519967717751437
+    - 41.84577837324427
     x13:
-    - -0.19920098627926822
-    - 0.5095640242994445
-    - -0.3729003029693749
+    - 51.50421179302046
     x14:
-    - 0.6242153953856313
-    - -1.0186442123275312
-    - -3.0604199544158504
+    - 15.40016168148747
     x15:
-    - -0.8808380741451728
-    - -1.2523426524049155
-    - -0.7973581049651146
+    - 16.958113054087026
     x16:
-    - 1.0661014648120772
-    - 0.028663896855952335
-    - 1.3294361695442827
+    - 7.016658253407371
     x17:
-    - -0.16809563986134812
-    - 1.5728684096371472
-    - 2.403793617769665
+    - 0.31728840754111515
     x18:
-    - -0.40971494857613056
-    - -2.0685626995449002
-    - -2.6352772611427193
-    x19:
-    - -0.48150986525698813
-    - 1.2364168667276738
-    - 2.180976940508645
+    - 0.37469684721099433
     x2:
-    - -0.6858149404655255
-    - -2.5704433172483783
-    - -0.2805726481338807
+    - 0.9987966305655837
     x3:
-    - -0.9938521434003649
-    - 0.004948433682649548
-    - 0.12386186534436842
+    - 0.9876543209876546
     x4:
-    - -1.5351676597000408
-    - -0.06686764679970754
-    - -1.1398384472701109
+    - 0.9621793942906827
     x5:
-    - 0.05599600888975593
-    - 0.7388591828858628
-    - -0.014210997630448757
+    - 0.9412654160563247
     x6:
-    - -0.14538476476312687
-    - 0.954051151587725
-    - 1.4942788646796958
+    - 0.9297844692386968
     x7:
-    - 1.705729977752746
-    - 0.45072299069455823
-    - -0.27356356357396394
+    - 23.85371179039301
     x8:
-    - 0.6083980206344903
-    - 0.500745387789729
-    - -0.13961381253754068
+    - 15.242242787152968
     x9:
-    - 0.3675427037708833
-    - -0.19653609210222556
-    - -2.081336638897732
+    - 78.08802650260293
 ---
 
 # Model description
@@ -107,26 +65,53 @@ widget:
 <details>
 <summary> Click to expand </summary>
 
-| Hyperparameter           | Value   |
-|--------------------------|---------|
-| ccp_alpha                | 0.0     |
-| class_weight             |         |
-| criterion                | gini    |
-| max_depth                |         |
-| max_features             |         |
-| max_leaf_nodes           |         |
-| min_impurity_decrease    | 0.0     |
-| min_samples_leaf         | 1       |
-| min_samples_split        | 2       |
-| min_weight_fraction_leaf | 0.0     |
-| random_state             |         |
-| splitter                 | best    |
+| Hyperparameter          | Value            |
+|-------------------------|------------------|
+| objective               | reg:squarederror |
+| base_score              |                  |
+| booster                 |                  |
+| callbacks               |                  |
+| colsample_bylevel       |                  |
+| colsample_bynode        |                  |
+| colsample_bytree        |                  |
+| device                  |                  |
+| early_stopping_rounds   |                  |
+| enable_categorical      | False            |
+| eval_metric             |                  |
+| feature_types           |                  |
+| gamma                   |                  |
+| grow_policy             |                  |
+| importance_type         |                  |
+| interaction_constraints |                  |
+| learning_rate           | 0.01             |
+| max_bin                 |                  |
+| max_cat_threshold       |                  |
+| max_cat_to_onehot       |                  |
+| max_delta_step          |                  |
+| max_depth               |                  |
+| max_leaves              |                  |
+| min_child_weight        |                  |
+| missing                 | nan              |
+| monotone_constraints    |                  |
+| multi_strategy          |                  |
+| n_estimators            |                  |
+| n_jobs                  |                  |
+| num_parallel_tree       |                  |
+| random_state            |                  |
+| reg_alpha               |                  |
+| reg_lambda              |                  |
+| sampling_method         |                  |
+| scale_pos_weight        |                  |
+| subsample               |                  |
+| tree_method             |                  |
+| validate_parameters     |                  |
+| verbosity               |                  |
 
 </details>
 
 ### Model Plot
 
-<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-5" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier()</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" checked><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>
+<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=0.01, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,multi_strategy=None, n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">XGBRegressor</label><div class="sk-toggleable__content"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=0.01, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,multi_strategy=None, n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>
 
 ## Evaluation Results
 

config.json

@@ -19,119 +19,75 @@
             "x15",
             "x16",
             "x17",
-            "x18",
-            "x19"
+            "x18"
         ],
         "environment": [
             "scikit-learn=1.2.2"
         ],
         "example_input": {
             "x0": [
-                -0.09392700711129204,
-                2.371755361225434,
-                0.9671947566078468
+                187785.0
             ],
             "x1": [
-                1.6715939059930913,
-                1.0187539498438483,
-                0.31581839557288527
+                0.0
             ],
             "x10": [
-                1.974051683532626,
-                0.5529279014966972,
-                1.4204609635013674
+                98.45860046637567
             ],
             "x11": [
-                0.2611945709034309,
-                0.6196937299076192,
-                1.190400503805864
+                61.657549555908474
             ],
             "x12": [
-                -0.10332312980738237,
-                -1.5650628809571772,
-                -1.519967717751437
+                41.84577837324427
             ],
             "x13": [
-                -0.19920098627926822,
-                0.5095640242994445,
-                -0.3729003029693749
+                51.50421179302046
             ],
             "x14": [
-                0.6242153953856313,
-                -1.0186442123275312,
-                -3.0604199544158504
+                15.40016168148747
             ],
             "x15": [
-                -0.8808380741451728,
-                -1.2523426524049155,
-                -0.7973581049651146
+                16.958113054087026
             ],
             "x16": [
-                1.0661014648120772,
-                0.028663896855952335,
-                1.3294361695442827
+                7.016658253407371
             ],
             "x17": [
-                -0.16809563986134812,
-                1.5728684096371472,
-                2.403793617769665
+                0.31728840754111515
             ],
             "x18": [
-                -0.40971494857613056,
-                -2.0685626995449002,
-                -2.6352772611427193
-            ],
-            "x19": [
-                -0.48150986525698813,
-                1.2364168667276738,
-                2.180976940508645
+                0.37469684721099433
             ],
             "x2": [
-                -0.6858149404655255,
-                -2.5704433172483783,
-                -0.2805726481338807
+                0.9987966305655837
             ],
             "x3": [
-                -0.9938521434003649,
-                0.004948433682649548,
-                0.12386186534436842
+                0.9876543209876546
             ],
             "x4": [
-                -1.5351676597000408,
-                -0.06686764679970754,
-                -1.1398384472701109
+                0.9621793942906827
             ],
             "x5": [
-                0.05599600888975593,
-                0.7388591828858628,
-                -0.014210997630448757
+                0.9412654160563247
             ],
             "x6": [
-                -0.14538476476312687,
-                0.954051151587725,
-                1.4942788646796958
+                0.9297844692386968
             ],
             "x7": [
-                1.705729977752746,
-                0.45072299069455823,
-                -0.27356356357396394
+                23.85371179039301
             ],
             "x8": [
-                0.6083980206344903,
-                0.500745387789729,
-                -0.13961381253754068
+                15.242242787152968
             ],
             "x9": [
-                0.3675427037708833,
-                -0.19653609210222556,
-                -2.081336638897732
+                78.08802650260293
             ]
         },
         "model": {
             "file": "model.pkl"
         },
         "model_format": "pickle",
-        "task": "tabular-classification",
+        "task": "tabular-regression",
         "use_intelex": false
     }
 }

model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:106972a76869ac523ec7e3f414ce1088126f3413e1d006d2fb88347187e4e291
-size 48369
+oid sha256:2080851379992a0ed9ba12265536f417aa3418b1b2f0998ed768a2dd599fc338
+size 494003