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--- |
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license: mit |
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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-classification |
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model_format: pickle |
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model_file: example.pkl |
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widget: |
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- 0.034772186515921795 |
|
x73: |
|
- -0.0026496385531753577 |
|
- -0.0020135460529990953 |
|
- -0.005721515652193274 |
|
x74: |
|
- 0.01580227384710009 |
|
- 0.0171732056679937 |
|
- 0.014299868601773158 |
|
x75: |
|
- -0.010361368782894566 |
|
- -0.007805873997989549 |
|
- -0.0053241264998279 |
|
x76: |
|
- 0.004377901401270007 |
|
- -0.001060409672395441 |
|
- 0.00043618881070331015 |
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x77: |
|
- -0.027662984007904484 |
|
- -0.019925292815870875 |
|
- -0.03737199204744549 |
|
x78: |
|
- 0.0015057601444477661 |
|
- -0.002008621604411216 |
|
- -0.004382469760323012 |
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x79: |
|
- -0.014017814817996315 |
|
- -0.00424693123892803 |
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- -0.00658157902868913 |
|
x8: |
|
- -0.0030763922097523594 |
|
- -0.004448985778607642 |
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- -0.003314519081269046 |
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x80: |
|
- -0.017626799813237542 |
|
- -0.025999023247736144 |
|
- -0.01971370873658021 |
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x81: |
|
- -0.005431035712382781 |
|
- -0.0047866087168881785 |
|
- -0.00606188935376445 |
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x82: |
|
- -0.013849051621584603 |
|
- -0.007186794954608998 |
|
- 0.0009773454509909411 |
|
x83: |
|
- 0.050844562088809254 |
|
- 0.05775839873329935 |
|
- 0.05449173608622341 |
|
x84: |
|
- 0.011785574384681793 |
|
- 0.00949219830058632 |
|
- 0.009678772044598763 |
|
x85: |
|
- -0.011198568124833315 |
|
- -0.005911438924508035 |
|
- 0.003059637927152734 |
|
x86: |
|
- 0.0018162463508459212 |
|
- -0.009619620192155743 |
|
- -0.005569531487438966 |
|
x87: |
|
- -0.04076314789831246 |
|
- -0.03461958177466854 |
|
- -0.04502472626605864 |
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x88: |
|
- -0.0013220903551483755 |
|
- -0.0045819948758560654 |
|
- -0.010932244504049407 |
|
x89: |
|
- -5.337108458985972e-05 |
|
- 0.0011962930129095022 |
|
- -0.005681979149866729 |
|
x9: |
|
- -0.0009517067051438168 |
|
- -0.0030529840692781426 |
|
- -0.005295939049015201 |
|
x90: |
|
- 0.0053464588950856515 |
|
- 0.009058721163688603 |
|
- 0.011819222113290043 |
|
x91: |
|
- -0.00018728668655116352 |
|
- -0.003407154233195461 |
|
- -0.0067378665428906165 |
|
x92: |
|
- -0.10257589466303413 |
|
- -0.10260039890010662 |
|
- -0.09988287037272972 |
|
x93: |
|
- -0.01839525849711251 |
|
- -0.016749222913921554 |
|
- -0.01686540155723277 |
|
x94: |
|
- 0.006420938070317661 |
|
- -0.005941226835858095 |
|
- -0.00738909172990437 |
|
x95: |
|
- 0.002506209068662046 |
|
- 0.019079323838524558 |
|
- 0.008266405205897711 |
|
x96: |
|
- 0.0063033792925848795 |
|
- 0.014768786145732026 |
|
- 0.00016222416576325754 |
|
x97: |
|
- -0.0037223830368446065 |
|
- -0.010105300441945174 |
|
- -0.005237242287451712 |
|
x98: |
|
- 0.03157597431185933 |
|
- 0.005399346857428037 |
|
- 0.017238489586075215 |
|
x99: |
|
- 0.007907370024882203 |
|
- 0.004592407031559381 |
|
- 0.0029258267052851067 |
|
--- |
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# Model description |
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[More Information Needed] |
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## Intended uses & limitations |
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[More Information Needed] |
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## Training Procedure |
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[More Information Needed] |
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### Hyperparameters |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|-------------------|------------| |
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| C | 10 | |
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| class_weight | | |
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| dual | False | |
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| fit_intercept | True | |
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| intercept_scaling | 1 | |
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| l1_ratio | | |
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| max_iter | 100 | |
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| multi_class | deprecated | |
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| n_jobs | | |
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| penalty | l2 | |
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| random_state | | |
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| solver | lbfgs | |
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| tol | 0.0001 | |
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| verbose | 0 | |
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| warm_start | False | |
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</details> |
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### Model Plot |
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<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;} |
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}#sk-container-id-5 {color: var(--sklearn-color-text); |
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}#sk-container-id-5 pre {padding: 0; |
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}#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; |
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}#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); |
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}#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; |
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}#sk-container-id-5 div.sk-text-repr-fallback {display: none; |
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}div.sk-parallel-item, |
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div.sk-serial, |
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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; |
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}/* 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; |
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}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; |
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}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column; |
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}#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; |
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}/* 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; |
|
}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is |
|
clickable and can be expanded/collapsed. |
|
- Pipeline and ColumnTransformer use this feature and define the default style |
|
- Estimators will overwrite some part of the style using the `sk-estimator` class |
|
*//* 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); |
|
}/* Toggleable label */ |
|
#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; |
|
}#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); |
|
}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); |
|
}/* 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); |
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}#sk-container-id-5 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}#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); |
|
}#sk-container-id-5 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto; |
|
}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; |
|
}/* 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); |
|
}#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); |
|
}/* Estimator-specific style *//* Colorize estimator box */ |
|
#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); |
|
}#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); |
|
}#sk-container-id-5 div.sk-label label.sk-toggleable__label, |
|
#sk-container-id-5 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background); |
|
}/* On hover, darken the color of the background */ |
|
#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); |
|
}/* Label box, darken color on hover, fitted */ |
|
#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); |
|
}/* Estimator label */#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; |
|
}/* Estimator-specific */ |
|
#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); |
|
}#sk-container-id-5 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}/* on hover */ |
|
#sk-container-id-5 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
|
}#sk-container-id-5 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2); |
|
}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link, |
|
a:link.sk-estimator-doc-link, |
|
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); |
|
}.sk-estimator-doc-link.fitted, |
|
a:link.sk-estimator-doc-link.fitted, |
|
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); |
|
}/* On hover */ |
|
div.sk-estimator:hover .sk-estimator-doc-link:hover, |
|
.sk-estimator-doc-link:hover, |
|
div.sk-label-container:hover .sk-estimator-doc-link:hover, |
|
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
|
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, |
|
.sk-estimator-doc-link.fitted:hover, |
|
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, |
|
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
|
}/* Span, style for the box shown on hovering the info icon */ |
|
.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); |
|
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3); |
|
}.sk-estimator-doc-link:hover span {display: block; |
|
}/* "?"-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; |
|
}#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); |
|
}/* On hover */ |
|
#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; |
|
}#sk-container-id-5 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); |
|
} |
|
</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> |
|
|
|
## Evaluation Results |
|
|
|
[More Information Needed] |
|
|
|
# How to Get Started with the Model |
|
|
|
[More Information Needed] |
|
|
|
# Model Card Authors |
|
|
|
This model card is written by following authors: |
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|
|
[More Information Needed] |
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|
|
# 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] |
|
``` |
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|
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# citation_bibtex |
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|
|
bibtex |
|
@inproceedings{...,year={2020}} |
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|
|
# get_started_code |
|
|
|
import pickle |
|
with open(dtc_pkl_filename, 'rb') as file: |
|
clf = pickle.load(file) |
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|
|
# model_card_authors |
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|
|
skops_user |
|
|
|
# limitations |
|
|
|
This model is not ready to be used in production. |
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|
|
# model_description |
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|
|
This is a LogisticRegression model trained on word2vec embeddings. |
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|
