---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: voting.pickle
widget:
- structuredData:
    NFS_IO_log10_MBps:
    - -3.0
    - -1.4805
    - -3.0
    local_IO_log10_MBps:
    - -0.8381
    - 0.0968
    - -0.9018
    memory_GB:
    - 43.5205
    - 10.3542
    - 88.2232
    network_log10_MBps:
    - -1.1597
    - 0.8827
    - -0.519
---

# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

[More Information Needed]

### Hyperparameters

<details>
<summary> Click to expand </summary>

| Hyperparameter               | Value                                                                                                                                                                                                                                             |
|------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| estimators                   | [('rf', RandomForestClassifier(random_state=12345)), ('lr', LogisticRegression(max_iter=1000, random_state=12345)), ('sgd', SGDClassifier(random_state=12345)), ('knn', KNeighborsClassifier()), ('ada', AdaBoostClassifier(random_state=12345))] |
| flatten_transform            | True                                                                                                                                                                                                                                              |
| n_jobs                       |                                                                                                                                                                                                                                                   |
| verbose                      | False                                                                                                                                                                                                                                             |
| voting                       | hard                                                                                                                                                                                                                                              |
| weights                      |                                                                                                                                                                                                                                                   |
| rf                           | RandomForestClassifier(random_state=12345)                                                                                                                                                                                                        |
| lr                           | LogisticRegression(max_iter=1000, random_state=12345)                                                                                                                                                                                             |
| sgd                          | SGDClassifier(random_state=12345)                                                                                                                                                                                                                 |
| knn                          | KNeighborsClassifier()                                                                                                                                                                                                                            |
| ada                          | AdaBoostClassifier(random_state=12345)                                                                                                                                                                                                            |
| rf__bootstrap                | True                                                                                                                                                                                                                                              |
| rf__ccp_alpha                | 0.0                                                                                                                                                                                                                                               |
| rf__class_weight             |                                                                                                                                                                                                                                                   |
| rf__criterion                | gini                                                                                                                                                                                                                                              |
| rf__max_depth                |                                                                                                                                                                                                                                                   |
| rf__max_features             | sqrt                                                                                                                                                                                                                                              |
| rf__max_leaf_nodes           |                                                                                                                                                                                                                                                   |
| rf__max_samples              |                                                                                                                                                                                                                                                   |
| rf__min_impurity_decrease    | 0.0                                                                                                                                                                                                                                               |
| rf__min_samples_leaf         | 1                                                                                                                                                                                                                                                 |
| rf__min_samples_split        | 2                                                                                                                                                                                                                                                 |
| rf__min_weight_fraction_leaf | 0.0                                                                                                                                                                                                                                               |
| rf__monotonic_cst            |                                                                                                                                                                                                                                                   |
| rf__n_estimators             | 100                                                                                                                                                                                                                                               |
| rf__n_jobs                   |                                                                                                                                                                                                                                                   |
| rf__oob_score                | False                                                                                                                                                                                                                                             |
| rf__random_state             | 12345                                                                                                                                                                                                                                             |
| rf__verbose                  | 0                                                                                                                                                                                                                                                 |
| rf__warm_start               | False                                                                                                                                                                                                                                             |
| lr__C                        | 1.0                                                                                                                                                                                                                                               |
| lr__class_weight             |                                                                                                                                                                                                                                                   |
| lr__dual                     | False                                                                                                                                                                                                                                             |
| lr__fit_intercept            | True                                                                                                                                                                                                                                              |
| lr__intercept_scaling        | 1                                                                                                                                                                                                                                                 |
| lr__l1_ratio                 |                                                                                                                                                                                                                                                   |
| lr__max_iter                 | 1000                                                                                                                                                                                                                                              |
| lr__multi_class              | deprecated                                                                                                                                                                                                                                        |
| lr__n_jobs                   |                                                                                                                                                                                                                                                   |
| lr__penalty                  | l2                                                                                                                                                                                                                                                |
| lr__random_state             | 12345                                                                                                                                                                                                                                             |
| lr__solver                   | lbfgs                                                                                                                                                                                                                                             |
| lr__tol                      | 0.0001                                                                                                                                                                                                                                            |
| lr__verbose                  | 0                                                                                                                                                                                                                                                 |
| lr__warm_start               | False                                                                                                                                                                                                                                             |
| sgd__alpha                   | 0.0001                                                                                                                                                                                                                                            |
| sgd__average                 | False                                                                                                                                                                                                                                             |
| sgd__class_weight            |                                                                                                                                                                                                                                                   |
| sgd__early_stopping          | False                                                                                                                                                                                                                                             |
| sgd__epsilon                 | 0.1                                                                                                                                                                                                                                               |
| sgd__eta0                    | 0.0                                                                                                                                                                                                                                               |
| sgd__fit_intercept           | True                                                                                                                                                                                                                                              |
| sgd__l1_ratio                | 0.15                                                                                                                                                                                                                                              |
| sgd__learning_rate           | optimal                                                                                                                                                                                                                                           |
| sgd__loss                    | hinge                                                                                                                                                                                                                                             |
| sgd__max_iter                | 1000                                                                                                                                                                                                                                              |
| sgd__n_iter_no_change        | 5                                                                                                                                                                                                                                                 |
| sgd__n_jobs                  |                                                                                                                                                                                                                                                   |
| sgd__penalty                 | l2                                                                                                                                                                                                                                                |
| sgd__power_t                 | 0.5                                                                                                                                                                                                                                               |
| sgd__random_state            | 12345                                                                                                                                                                                                                                             |
| sgd__shuffle                 | True                                                                                                                                                                                                                                              |
| sgd__tol                     | 0.001                                                                                                                                                                                                                                             |
| sgd__validation_fraction     | 0.1                                                                                                                                                                                                                                               |
| sgd__verbose                 | 0                                                                                                                                                                                                                                                 |
| sgd__warm_start              | False                                                                                                                                                                                                                                             |
| knn__algorithm               | auto                                                                                                                                                                                                                                              |
| knn__leaf_size               | 30                                                                                                                                                                                                                                                |
| knn__metric                  | minkowski                                                                                                                                                                                                                                         |
| knn__metric_params           |                                                                                                                                                                                                                                                   |
| knn__n_jobs                  |                                                                                                                                                                                                                                                   |
| knn__n_neighbors             | 5                                                                                                                                                                                                                                                 |
| knn__p                       | 2                                                                                                                                                                                                                                                 |
| knn__weights                 | uniform                                                                                                                                                                                                                                           |
| ada__algorithm               | deprecated                                                                                                                                                                                                                                        |
| ada__estimator               |                                                                                                                                                                                                                                                   |
| ada__learning_rate           | 1.0                                                                                                                                                                                                                                               |
| ada__n_estimators            | 50                                                                                                                                                                                                                                                |
| ada__random_state            | 12345                                                                                                                                                                                                                                             |

</details>

### Model Plot

<style>#sk-container-id-5 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: #000;--sklearn-color-text-muted: #666;--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-5 {color: var(--sklearn-color-text);
}#sk-container-id-5 pre {padding: 0;
}#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-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-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;
}#sk-container-id-5 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-5 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-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;
}#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;
}/* 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: flex;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;align-items: start;justify-content: space-between;gap: 0.5em;
}#sk-container-id-5 label.sk-toggleable__label .caption {font-size: 0.6rem;font-weight: lighter;color: var(--sklearn-color-text-muted);
}#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);
}#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: 0.5em;text-align: center;/* 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>VotingClassifier(estimators=[(&#x27;rf&#x27;, RandomForestClassifier(random_state=12345)),(&#x27;lr&#x27;,LogisticRegression(max_iter=1000,random_state=12345)),(&#x27;sgd&#x27;, SGDClassifier(random_state=12345)),(&#x27;knn&#x27;, KNeighborsClassifier()),(&#x27;ada&#x27;, AdaBoostClassifier(random_state=12345))])</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-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>VotingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.VotingClassifier.html">?<span>Documentation for VotingClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>VotingClassifier(estimators=[(&#x27;rf&#x27;, RandomForestClassifier(random_state=12345)),(&#x27;lr&#x27;,LogisticRegression(max_iter=1000,random_state=12345)),(&#x27;sgd&#x27;, SGDClassifier(random_state=12345)),(&#x27;knn&#x27;, KNeighborsClassifier()),(&#x27;ada&#x27;, AdaBoostClassifier(random_state=12345))])</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"><label>rf</label></div></div><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-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(random_state=12345)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>lr</label></div></div><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-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(max_iter=1000, random_state=12345)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>sgd</label></div></div><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-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SGDClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.SGDClassifier.html">?<span>Documentation for SGDClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SGDClassifier(random_state=12345)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>knn</label></div></div><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-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>KNeighborsClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>ada</label></div></div><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-30" type="checkbox" ><label for="sk-estimator-id-30" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>AdaBoostClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.AdaBoostClassifier.html">?<span>Documentation for AdaBoostClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>AdaBoostClassifier(random_state=12345)</pre></div> </div></div></div></div></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:

[More Information Needed]

# Model Card Contact

You can contact the model card authors through following channels:
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# Citation

Below you can find information related to citation.

**BibTeX:**
```
[More Information Needed]
```

# citation_bibtex

to be done

# get_started_code

None

# model_card_authors

Syreeta, Shraddha, Sravani, Sadhana, Ranjitha

# limitations

Not handling logs

# model_description

Failure prediction and remediation