pushing files to the repo from the example!
Browse files- README.md +1380 -0
- config.json +1817 -0
- example.pkl +3 -0
README.md
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|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
library_name: sklearn
|
| 4 |
+
tags:
|
| 5 |
+
- sklearn
|
| 6 |
+
- skops
|
| 7 |
+
- tabular-classification
|
| 8 |
+
model_format: pickle
|
| 9 |
+
model_file: example.pkl
|
| 10 |
+
widget:
|
| 11 |
+
- structuredData:
|
| 12 |
+
x0:
|
| 13 |
+
- 0.009092870334671179
|
| 14 |
+
- 0.003102656001949986
|
| 15 |
+
- 0.015391897543493002
|
| 16 |
+
x1:
|
| 17 |
+
- -0.008587878452047532
|
| 18 |
+
- -0.004821557006248149
|
| 19 |
+
- -0.008642960900302839
|
| 20 |
+
x10:
|
| 21 |
+
- -0.012300542400784284
|
| 22 |
+
- -0.011120240403533675
|
| 23 |
+
- -0.013556428539299371
|
| 24 |
+
x100:
|
| 25 |
+
- 0.0018619135882853863
|
| 26 |
+
- 0.00930385729043337
|
| 27 |
+
- 0.00789834606493295
|
| 28 |
+
x101:
|
| 29 |
+
- -0.007031379649156911
|
| 30 |
+
- -0.006770084715010186
|
| 31 |
+
- -0.007879321328019379
|
| 32 |
+
x102:
|
| 33 |
+
- 0.009378332276946023
|
| 34 |
+
- 0.008652020103712176
|
| 35 |
+
- 0.004937538538662785
|
| 36 |
+
x103:
|
| 37 |
+
- -0.00017358270661506464
|
| 38 |
+
- 0.00746881935872789
|
| 39 |
+
- 0.0020782344342647036
|
| 40 |
+
x104:
|
| 41 |
+
- -0.006695491598303939
|
| 42 |
+
- -0.0007450853301248894
|
| 43 |
+
- -0.01323058220953465
|
| 44 |
+
x105:
|
| 45 |
+
- -0.10004859369885373
|
| 46 |
+
- -0.10381869721602593
|
| 47 |
+
- -0.10533102167634449
|
| 48 |
+
x106:
|
| 49 |
+
- 0.005158763673215729
|
| 50 |
+
- 0.012021561953014914
|
| 51 |
+
- 0.004614050596029739
|
| 52 |
+
x107:
|
| 53 |
+
- -0.004830967212144874
|
| 54 |
+
- -0.009490713552761654
|
| 55 |
+
- -0.01741581768459313
|
| 56 |
+
x108:
|
| 57 |
+
- 0.0156907184815037
|
| 58 |
+
- 0.00956848459689605
|
| 59 |
+
- 0.011831864226109315
|
| 60 |
+
x109:
|
| 61 |
+
- -0.004351239300376619
|
| 62 |
+
- 0.006921653748523464
|
| 63 |
+
- 0.0010901811571481328
|
| 64 |
+
x11:
|
| 65 |
+
- -0.07784159693830206
|
| 66 |
+
- -0.07089506365069799
|
| 67 |
+
- -0.05081191153138341
|
| 68 |
+
x110:
|
| 69 |
+
- 0.0026529473038145192
|
| 70 |
+
- -0.009755768627243741
|
| 71 |
+
- 0.0024120312533341347
|
| 72 |
+
x111:
|
| 73 |
+
- 0.08272518580255224
|
| 74 |
+
- 0.07430779747378191
|
| 75 |
+
- 0.06866007077273766
|
| 76 |
+
x112:
|
| 77 |
+
- 0.0685310028847416
|
| 78 |
+
- 0.06857653596069149
|
| 79 |
+
- 0.0686577236818805
|
| 80 |
+
x113:
|
| 81 |
+
- -0.022115351643130096
|
| 82 |
+
- -0.015770825940491676
|
| 83 |
+
- -0.015027861268242338
|
| 84 |
+
x114:
|
| 85 |
+
- -0.01135826670382259
|
| 86 |
+
- 0.0003449911294101274
|
| 87 |
+
- 0.0021022874088009336
|
| 88 |
+
x115:
|
| 89 |
+
- 2.1160523754705222e-05
|
| 90 |
+
- -0.013460295479834995
|
| 91 |
+
- -0.005601419438877471
|
| 92 |
+
x116:
|
| 93 |
+
- -0.023279981265574345
|
| 94 |
+
- -0.02109213508943697
|
| 95 |
+
- -0.019275555286569417
|
| 96 |
+
x117:
|
| 97 |
+
- -0.0011759051834049291
|
| 98 |
+
- -0.0059930860200263746
|
| 99 |
+
- 0.0032645273084872988
|
| 100 |
+
x118:
|
| 101 |
+
- -0.026989715361824037
|
| 102 |
+
- -0.027874729447261767
|
| 103 |
+
- -0.024370422278298064
|
| 104 |
+
x119:
|
| 105 |
+
- -0.006996300949358343
|
| 106 |
+
- 0.0011684184752317522
|
| 107 |
+
- -0.0023379682877997833
|
| 108 |
+
x12:
|
| 109 |
+
- 0.014413854403234746
|
| 110 |
+
- 0.013602644348694981
|
| 111 |
+
- 0.01750015340814436
|
| 112 |
+
x120:
|
| 113 |
+
- -0.0011770071047194825
|
| 114 |
+
- 0.0018298863659823874
|
| 115 |
+
- 0.003323454433659747
|
| 116 |
+
x121:
|
| 117 |
+
- 0.008535631768970835
|
| 118 |
+
- -0.006115621721484983
|
| 119 |
+
- -0.002154990320128423
|
| 120 |
+
x122:
|
| 121 |
+
- 0.015322618239405374
|
| 122 |
+
- 0.011710183835477858
|
| 123 |
+
- 0.007894899991434714
|
| 124 |
+
x123:
|
| 125 |
+
- -0.015022693105774981
|
| 126 |
+
- -0.021068194691834997
|
| 127 |
+
- -0.018124888243619353
|
| 128 |
+
x124:
|
| 129 |
+
- 0.009068085221105327
|
| 130 |
+
- 0.022779395773415017
|
| 131 |
+
- 0.01648503082062773
|
| 132 |
+
x125:
|
| 133 |
+
- -0.0006781558254553272
|
| 134 |
+
- -0.0003334554654931457
|
| 135 |
+
- -0.008157297613689917
|
| 136 |
+
x126:
|
| 137 |
+
- -0.02312681346580969
|
| 138 |
+
- -0.014167446730781763
|
| 139 |
+
- -0.014498392273229977
|
| 140 |
+
x127:
|
| 141 |
+
- -0.08542760907010616
|
| 142 |
+
- -0.09335869247250929
|
| 143 |
+
- -0.10574591490375396
|
| 144 |
+
x128:
|
| 145 |
+
- 0.016530213366624795
|
| 146 |
+
- 0.026873074126073728
|
| 147 |
+
- 0.016434475040511678
|
| 148 |
+
x129:
|
| 149 |
+
- 0.10532287152059246
|
| 150 |
+
- 0.10921992696684056
|
| 151 |
+
- 0.10282375669910969
|
| 152 |
+
x13:
|
| 153 |
+
- -0.010105885980563641
|
| 154 |
+
- -0.003523872900412136
|
| 155 |
+
- 0.0024316665633199995
|
| 156 |
+
x130:
|
| 157 |
+
- 0.0030662936310975526
|
| 158 |
+
- 0.010879987778527469
|
| 159 |
+
- 0.015215802599926031
|
| 160 |
+
x131:
|
| 161 |
+
- 0.03653688440528257
|
| 162 |
+
- 0.023596695940849816
|
| 163 |
+
- 0.023896596409138982
|
| 164 |
+
x132:
|
| 165 |
+
- -0.0019008446774480875
|
| 166 |
+
- -0.004911443676436526
|
| 167 |
+
- 0.001457805165814029
|
| 168 |
+
x133:
|
| 169 |
+
- -0.00033889620221759146
|
| 170 |
+
- -0.004523778530244895
|
| 171 |
+
- 0.00016223582745253763
|
| 172 |
+
x134:
|
| 173 |
+
- 0.01314741143828781
|
| 174 |
+
- -0.004293095444217184
|
| 175 |
+
- 0.009541051113046706
|
| 176 |
+
x135:
|
| 177 |
+
- 0.0056414561515009546
|
| 178 |
+
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| 1212 |
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---
|
| 1213 |
+
|
| 1214 |
+
# Model description
|
| 1215 |
+
|
| 1216 |
+
[More Information Needed]
|
| 1217 |
+
|
| 1218 |
+
## Intended uses & limitations
|
| 1219 |
+
|
| 1220 |
+
[More Information Needed]
|
| 1221 |
+
|
| 1222 |
+
## Training Procedure
|
| 1223 |
+
|
| 1224 |
+
[More Information Needed]
|
| 1225 |
+
|
| 1226 |
+
### Hyperparameters
|
| 1227 |
+
|
| 1228 |
+
<details>
|
| 1229 |
+
<summary> Click to expand </summary>
|
| 1230 |
+
|
| 1231 |
+
| Hyperparameter | Value |
|
| 1232 |
+
|-------------------|------------|
|
| 1233 |
+
| C | 10 |
|
| 1234 |
+
| class_weight | |
|
| 1235 |
+
| dual | False |
|
| 1236 |
+
| fit_intercept | True |
|
| 1237 |
+
| intercept_scaling | 1 |
|
| 1238 |
+
| l1_ratio | |
|
| 1239 |
+
| max_iter | 100 |
|
| 1240 |
+
| multi_class | deprecated |
|
| 1241 |
+
| n_jobs | |
|
| 1242 |
+
| penalty | l2 |
|
| 1243 |
+
| random_state | |
|
| 1244 |
+
| solver | lbfgs |
|
| 1245 |
+
| tol | 0.0001 |
|
| 1246 |
+
| verbose | 0 |
|
| 1247 |
+
| warm_start | False |
|
| 1248 |
+
|
| 1249 |
+
</details>
|
| 1250 |
+
|
| 1251 |
+
### Model Plot
|
| 1252 |
+
|
| 1253 |
+
<style>#sk-container-id-5 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
|
| 1254 |
+
}#sk-container-id-5 {color: var(--sklearn-color-text);
|
| 1255 |
+
}#sk-container-id-5 pre {padding: 0;
|
| 1256 |
+
}#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;
|
| 1257 |
+
}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
|
| 1258 |
+
}#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 thedefault 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;
|
| 1259 |
+
}#sk-container-id-5 div.sk-text-repr-fallback {display: none;
|
| 1260 |
+
}div.sk-parallel-item,
|
| 1261 |
+
div.sk-serial,
|
| 1262 |
+
div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
|
| 1263 |
+
}/* Parallel-specific style estimator block */#sk-container-id-5 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
|
| 1264 |
+
}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
|
| 1265 |
+
}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;
|
| 1266 |
+
}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
|
| 1267 |
+
}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
|
| 1268 |
+
}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;
|
| 1269 |
+
}/* Serial-specific style estimator block */#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
|
| 1270 |
+
}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 1271 |
+
clickable and can be expanded/collapsed.
|
| 1272 |
+
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 1273 |
+
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 1274 |
+
*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-5 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
|
| 1275 |
+
}/* Toggleable label */
|
| 1276 |
+
#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
|
| 1277 |
+
}#sk-container-id-5 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
|
| 1278 |
+
}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
|
| 1279 |
+
}/* Toggleable content - dropdown */#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
|
| 1280 |
+
}#sk-container-id-5 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 1281 |
+
}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
|
| 1282 |
+
}#sk-container-id-5 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 1283 |
+
}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
|
| 1284 |
+
}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
|
| 1285 |
+
}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
|
| 1286 |
+
}#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
|
| 1287 |
+
}/* Estimator-specific style *//* Colorize estimator box */
|
| 1288 |
+
#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
|
| 1289 |
+
}#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
|
| 1290 |
+
}#sk-container-id-5 div.sk-label label.sk-toggleable__label,
|
| 1291 |
+
#sk-container-id-5 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
|
| 1292 |
+
}/* On hover, darken the color of the background */
|
| 1293 |
+
#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
|
| 1294 |
+
}/* Label box, darken color on hover, fitted */
|
| 1295 |
+
#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
|
| 1296 |
+
}/* Estimator label */#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
|
| 1297 |
+
}#sk-container-id-5 div.sk-label-container {text-align: center;
|
| 1298 |
+
}/* Estimator-specific */
|
| 1299 |
+
#sk-container-id-5 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
|
| 1300 |
+
}#sk-container-id-5 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 1301 |
+
}/* on hover */
|
| 1302 |
+
#sk-container-id-5 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
|
| 1303 |
+
}#sk-container-id-5 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
|
| 1304 |
+
}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
|
| 1305 |
+
a:link.sk-estimator-doc-link,
|
| 1306 |
+
a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
|
| 1307 |
+
}.sk-estimator-doc-link.fitted,
|
| 1308 |
+
a:link.sk-estimator-doc-link.fitted,
|
| 1309 |
+
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 1310 |
+
}/* On hover */
|
| 1311 |
+
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 1312 |
+
.sk-estimator-doc-link:hover,
|
| 1313 |
+
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 1314 |
+
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 1315 |
+
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 1316 |
+
.sk-estimator-doc-link.fitted:hover,
|
| 1317 |
+
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 1318 |
+
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 1319 |
+
}/* Span, style for the box shown on hovering the info icon */
|
| 1320 |
+
.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
| 1321 |
+
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
|
| 1322 |
+
}.sk-estimator-doc-link:hover span {display: block;
|
| 1323 |
+
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-5 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 1324 |
+
}#sk-container-id-5 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 1325 |
+
}/* On hover */
|
| 1326 |
+
#sk-container-id-5 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 1327 |
+
}#sk-container-id-5 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
|
| 1328 |
+
}
|
| 1329 |
+
</style><div id="sk-container-id-5" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=10)</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 fitted 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 fitted sk-toggleable__label-arrow fitted"> LogisticRegression<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=10)</pre></div> </div></div></div></div>
|
| 1330 |
+
|
| 1331 |
+
## Evaluation Results
|
| 1332 |
+
|
| 1333 |
+
[More Information Needed]
|
| 1334 |
+
|
| 1335 |
+
# How to Get Started with the Model
|
| 1336 |
+
|
| 1337 |
+
[More Information Needed]
|
| 1338 |
+
|
| 1339 |
+
# Model Card Authors
|
| 1340 |
+
|
| 1341 |
+
This model card is written by following authors:
|
| 1342 |
+
|
| 1343 |
+
[More Information Needed]
|
| 1344 |
+
|
| 1345 |
+
# Model Card Contact
|
| 1346 |
+
|
| 1347 |
+
You can contact the model card authors through following channels:
|
| 1348 |
+
[More Information Needed]
|
| 1349 |
+
|
| 1350 |
+
# Citation
|
| 1351 |
+
|
| 1352 |
+
Below you can find information related to citation.
|
| 1353 |
+
|
| 1354 |
+
**BibTeX:**
|
| 1355 |
+
```
|
| 1356 |
+
[More Information Needed]
|
| 1357 |
+
```
|
| 1358 |
+
|
| 1359 |
+
# citation_bibtex
|
| 1360 |
+
|
| 1361 |
+
bibtex
|
| 1362 |
+
@inproceedings{...,year={2020}}
|
| 1363 |
+
|
| 1364 |
+
# get_started_code
|
| 1365 |
+
|
| 1366 |
+
import pickle
|
| 1367 |
+
with open(dtc_pkl_filename, 'rb') as file:
|
| 1368 |
+
clf = pickle.load(file)
|
| 1369 |
+
|
| 1370 |
+
# model_card_authors
|
| 1371 |
+
|
| 1372 |
+
skops_user
|
| 1373 |
+
|
| 1374 |
+
# limitations
|
| 1375 |
+
|
| 1376 |
+
This model is not ready to be used in production.
|
| 1377 |
+
|
| 1378 |
+
# model_description
|
| 1379 |
+
|
| 1380 |
+
This is a LogisticRegression model trained on word2vec embeddings.
|
config.json
ADDED
|
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|
| 1 |
+
{
|
| 2 |
+
"sklearn": {
|
| 3 |
+
"columns": [
|
| 4 |
+
"x0",
|
| 5 |
+
"x1",
|
| 6 |
+
"x2",
|
| 7 |
+
"x3",
|
| 8 |
+
"x4",
|
| 9 |
+
"x5",
|
| 10 |
+
"x6",
|
| 11 |
+
"x7",
|
| 12 |
+
"x8",
|
| 13 |
+
"x9",
|
| 14 |
+
"x10",
|
| 15 |
+
"x11",
|
| 16 |
+
"x12",
|
| 17 |
+
"x13",
|
| 18 |
+
"x14",
|
| 19 |
+
"x15",
|
| 20 |
+
"x16",
|
| 21 |
+
"x17",
|
| 22 |
+
"x18",
|
| 23 |
+
"x19",
|
| 24 |
+
"x20",
|
| 25 |
+
"x21",
|
| 26 |
+
"x22",
|
| 27 |
+
"x23",
|
| 28 |
+
"x24",
|
| 29 |
+
"x25",
|
| 30 |
+
"x26",
|
| 31 |
+
"x27",
|
| 32 |
+
"x28",
|
| 33 |
+
"x29",
|
| 34 |
+
"x30",
|
| 35 |
+
"x31",
|
| 36 |
+
"x32",
|
| 37 |
+
"x33",
|
| 38 |
+
"x34",
|
| 39 |
+
"x35",
|
| 40 |
+
"x36",
|
| 41 |
+
"x37",
|
| 42 |
+
"x38",
|
| 43 |
+
"x39",
|
| 44 |
+
"x40",
|
| 45 |
+
"x41",
|
| 46 |
+
"x42",
|
| 47 |
+
"x43",
|
| 48 |
+
"x44",
|
| 49 |
+
"x45",
|
| 50 |
+
"x46",
|
| 51 |
+
"x47",
|
| 52 |
+
"x48",
|
| 53 |
+
"x49",
|
| 54 |
+
"x50",
|
| 55 |
+
"x51",
|
| 56 |
+
"x52",
|
| 57 |
+
"x53",
|
| 58 |
+
"x54",
|
| 59 |
+
"x55",
|
| 60 |
+
"x56",
|
| 61 |
+
"x57",
|
| 62 |
+
"x58",
|
| 63 |
+
"x59",
|
| 64 |
+
"x60",
|
| 65 |
+
"x61",
|
| 66 |
+
"x62",
|
| 67 |
+
"x63",
|
| 68 |
+
"x64",
|
| 69 |
+
"x65",
|
| 70 |
+
"x66",
|
| 71 |
+
"x67",
|
| 72 |
+
"x68",
|
| 73 |
+
"x69",
|
| 74 |
+
"x70",
|
| 75 |
+
"x71",
|
| 76 |
+
"x72",
|
| 77 |
+
"x73",
|
| 78 |
+
"x74",
|
| 79 |
+
"x75",
|
| 80 |
+
"x76",
|
| 81 |
+
"x77",
|
| 82 |
+
"x78",
|
| 83 |
+
"x79",
|
| 84 |
+
"x80",
|
| 85 |
+
"x81",
|
| 86 |
+
"x82",
|
| 87 |
+
"x83",
|
| 88 |
+
"x84",
|
| 89 |
+
"x85",
|
| 90 |
+
"x86",
|
| 91 |
+
"x87",
|
| 92 |
+
"x88",
|
| 93 |
+
"x89",
|
| 94 |
+
"x90",
|
| 95 |
+
"x91",
|
| 96 |
+
"x92",
|
| 97 |
+
"x93",
|
| 98 |
+
"x94",
|
| 99 |
+
"x95",
|
| 100 |
+
"x96",
|
| 101 |
+
"x97",
|
| 102 |
+
"x98",
|
| 103 |
+
"x99",
|
| 104 |
+
"x100",
|
| 105 |
+
"x101",
|
| 106 |
+
"x102",
|
| 107 |
+
"x103",
|
| 108 |
+
"x104",
|
| 109 |
+
"x105",
|
| 110 |
+
"x106",
|
| 111 |
+
"x107",
|
| 112 |
+
"x108",
|
| 113 |
+
"x109",
|
| 114 |
+
"x110",
|
| 115 |
+
"x111",
|
| 116 |
+
"x112",
|
| 117 |
+
"x113",
|
| 118 |
+
"x114",
|
| 119 |
+
"x115",
|
| 120 |
+
"x116",
|
| 121 |
+
"x117",
|
| 122 |
+
"x118",
|
| 123 |
+
"x119",
|
| 124 |
+
"x120",
|
| 125 |
+
"x121",
|
| 126 |
+
"x122",
|
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"task": "tabular-classification",
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|
example.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fa710cd4552d5b70c857d1651ca96e17adff80cdde13d4188867b688235e740a
|
| 3 |
+
size 317199
|