|
--- |
|
library_name: sklearn |
|
license: mit |
|
tags: |
|
- sklearn |
|
- skops |
|
- text-classification |
|
model_format: pickle |
|
model_file: skops-zquiq5g5.pkl |
|
--- |
|
|
|
# Model description |
|
|
|
This is a `Support Vector Classifier` model trained on SIRIUS dataset.As input, the model takes text embeddings encoded with camembert-base (768 tokens) |
|
|
|
## 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 | [('columntransformer', ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()),<br /> ('pca',<br /> PCA(n_components=84))]),<br /> Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br /> 'avg_9', 'avg_10',<br /> ...<br /> 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br /> 'max_765', 'max_766', 'max_767', 'max_768'],<br /> dtype='object', length=2304))],<br /> verbose_feature_names_out=False)), ('svc', SVC(probability=True, random_state=42))] | |
|
| verbose | False | |
|
| columntransformer | ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()),<br /> ('pca',<br /> PCA(n_components=84))]),<br /> Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br /> 'avg_9', 'avg_10',<br /> ...<br /> 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br /> 'max_765', 'max_766', 'max_767', 'max_768'],<br /> dtype='object', length=2304))],<br /> verbose_feature_names_out=False) | |
|
| svc | SVC(probability=True, random_state=42) | |
|
| columntransformer__force_int_remainder_cols | True | |
|
| columntransformer__n_jobs | | |
|
| columntransformer__remainder | drop | |
|
| columntransformer__sparse_threshold | 0.3 | |
|
| columntransformer__transformer_weights | | |
|
| columntransformer__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()), ('pca', PCA(n_components=84))]), Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8',<br /> 'avg_9', 'avg_10',<br /> ...<br /> 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764',<br /> 'max_765', 'max_766', 'max_767', 'max_768'],<br /> dtype='object', length=2304))] | |
|
| columntransformer__verbose | False | |
|
| columntransformer__verbose_feature_names_out | False | |
|
| columntransformer__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler()), ('pca', PCA(n_components=84))]) | |
|
| columntransformer__num__memory | | |
|
| columntransformer__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=84))] | |
|
| columntransformer__num__verbose | False | |
|
| columntransformer__num__imputer | SimpleImputer(strategy='median') | |
|
| columntransformer__num__scaler | StandardScaler() | |
|
| columntransformer__num__pca | PCA(n_components=84) | |
|
| columntransformer__num__imputer__add_indicator | False | |
|
| columntransformer__num__imputer__copy | True | |
|
| columntransformer__num__imputer__fill_value | | |
|
| columntransformer__num__imputer__keep_empty_features | False | |
|
| columntransformer__num__imputer__missing_values | nan | |
|
| columntransformer__num__imputer__strategy | median | |
|
| columntransformer__num__scaler__copy | True | |
|
| columntransformer__num__scaler__with_mean | True | |
|
| columntransformer__num__scaler__with_std | True | |
|
| columntransformer__num__pca__copy | True | |
|
| columntransformer__num__pca__iterated_power | auto | |
|
| columntransformer__num__pca__n_components | 84 | |
|
| columntransformer__num__pca__n_oversamples | 10 | |
|
| columntransformer__num__pca__power_iteration_normalizer | auto | |
|
| columntransformer__num__pca__random_state | | |
|
| columntransformer__num__pca__svd_solver | auto | |
|
| columntransformer__num__pca__tol | 0.0 | |
|
| columntransformer__num__pca__whiten | False | |
|
| svc__C | 1.0 | |
|
| svc__break_ties | False | |
|
| svc__cache_size | 200 | |
|
| svc__class_weight | | |
|
| svc__coef0 | 0.0 | |
|
| svc__decision_function_shape | ovr | |
|
| svc__degree | 3 | |
|
| svc__gamma | scale | |
|
| svc__kernel | rbf | |
|
| svc__max_iter | -1 | |
|
| svc__probability | True | |
|
| svc__random_state | 42 | |
|
| svc__shrinking | True | |
|
| svc__tol | 0.001 | |
|
| svc__verbose | False | |
|
|
|
</details> |
|
|
|
### Model Plot |
|
|
|
<style>#sk-container-id-2 {/* 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;} |
|
}#sk-container-id-2 {color: var(--sklearn-color-text); |
|
}#sk-container-id-2 pre {padding: 0; |
|
}#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-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); |
|
}#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 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; |
|
}#sk-container-id-2 div.sk-text-repr-fallback {display: none; |
|
}div.sk-parallel-item, |
|
div.sk-serial, |
|
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; |
|
}/* Parallel-specific style estimator block */#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1; |
|
}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; |
|
}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column; |
|
}#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; |
|
}/* Serial-specific style estimator block */#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); |
|
}/* Toggleable content - dropdown */#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
|
}#sk-container-id-2 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto; |
|
}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; |
|
}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-2 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-2 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-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
|
}#sk-container-id-2 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-2 div.sk-label label.sk-toggleable__label, |
|
#sk-container-id-2 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-2 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-2 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-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; |
|
}/* Estimator-specific */ |
|
#sk-container-id-2 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-2 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}/* on hover */ |
|
#sk-container-id-2 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
|
}#sk-container-id-2 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-2 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-2 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-2 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-2 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); |
|
} |
|
</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler()),('pca',PCA(n_components=84))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304))],verbose_feature_names_out=False)),('svc', SVC(probability=True, random_state=42))])</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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler()),('pca',PCA(n_components=84))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304))],verbose_feature_names_out=False)),('svc', SVC(probability=True, random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> columntransformer: ColumnTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler()),('pca',PCA(n_components=84))]),Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304))],verbose_feature_names_out=False)</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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">num</label><div class="sk-toggleable__content fitted"><pre>Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8','avg_9', 'avg_10',...'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764','max_765', 'max_766', 'max_767', 'max_768'],dtype='object', length=2304)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy='median')</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> StandardScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-13" type="checkbox" ><label for="sk-estimator-id-13" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> PCA<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></label><div class="sk-toggleable__content fitted"><pre>PCA(n_components=84)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-14" type="checkbox" ><label for="sk-estimator-id-14" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> SVC<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></label><div class="sk-toggleable__content fitted"><pre>SVC(probability=True, random_state=42)</pre></div> </div></div></div></div></div></div> |
|
|
|
## Evaluation Results |
|
|
|
| Metric | Value | |
|
|----------|----------| |
|
| accuracy | 0.935065 | |
|
| f1 score | 0.935709 | |
|
|
|
### Confusion Matrix |
|
|
|
 |
|
|
|
# How to Get Started with the Model |
|
|
|
[More Information Needed] |
|
|
|
# Model Card Authors |
|
|
|
huynhdoo |
|
|
|
# Model Card Contact |
|
|
|
You can contact the model card authors through following channels: |
|
[More Information Needed] |
|
|
|
# Citation |
|
|
|
**BibTeX** |
|
|
|
``` |
|
@inproceedings{...,year={2024}} |
|
``` |
|
|
|
# get_started_code |
|
|
|
import pickle as pickle |
|
with open(pkl_filename, 'rb') as file: |
|
pipe = pickle.load(file) |
|
|
