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---
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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: voting.pickle
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widget:
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- structuredData:
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NFS_IO_log10_MBps:
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- -3.0
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- -1.4805
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- -3.0
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local_IO_log10_MBps:
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- -0.8381
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- 0.0968
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- -0.9018
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memory_GB:
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- 43.5205
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- 10.3542
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- 88.2232
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network_log10_MBps:
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- -1.1597
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- 0.8827
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- -0.519
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---
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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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| 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))] |
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| flatten_transform | True |
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| n_jobs | |
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| verbose | False |
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| voting | hard |
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| weights | |
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| rf | RandomForestClassifier(random_state=12345) |
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| lr | LogisticRegression(max_iter=1000, random_state=12345) |
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| sgd | SGDClassifier(random_state=12345) |
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| knn | KNeighborsClassifier() |
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| ada | AdaBoostClassifier(random_state=12345) |
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| rf__bootstrap | True |
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| rf__ccp_alpha | 0.0 |
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| rf__class_weight | |
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| rf__criterion | gini |
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| rf__max_depth | |
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| rf__max_features | sqrt |
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| rf__max_leaf_nodes | |
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| rf__max_samples | |
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| rf__min_impurity_decrease | 0.0 |
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| rf__min_samples_leaf | 1 |
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| rf__min_samples_split | 2 |
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| rf__min_weight_fraction_leaf | 0.0 |
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| rf__monotonic_cst | |
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| rf__n_estimators | 100 |
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| rf__n_jobs | |
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| rf__oob_score | False |
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| rf__random_state | 12345 |
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| rf__verbose | 0 |
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| rf__warm_start | False |
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| lr__C | 1.0 |
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| lr__class_weight | |
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| lr__dual | False |
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| lr__fit_intercept | True |
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| lr__intercept_scaling | 1 |
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| lr__l1_ratio | |
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| lr__max_iter | 1000 |
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| lr__multi_class | deprecated |
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| lr__n_jobs | |
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| lr__penalty | l2 |
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| lr__random_state | 12345 |
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| lr__solver | lbfgs |
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| lr__tol | 0.0001 |
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| lr__verbose | 0 |
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| lr__warm_start | False |
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| sgd__alpha | 0.0001 |
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| sgd__average | False |
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| sgd__class_weight | |
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| sgd__early_stopping | False |
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| sgd__epsilon | 0.1 |
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| sgd__eta0 | 0.0 |
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| sgd__fit_intercept | True |
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| sgd__l1_ratio | 0.15 |
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| sgd__learning_rate | optimal |
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| sgd__loss | hinge |
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| sgd__max_iter | 1000 |
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| sgd__n_iter_no_change | 5 |
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| sgd__n_jobs | |
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| sgd__penalty | l2 |
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| sgd__power_t | 0.5 |
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| sgd__random_state | 12345 |
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| sgd__shuffle | True |
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| sgd__tol | 0.001 |
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| sgd__validation_fraction | 0.1 |
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| sgd__verbose | 0 |
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| sgd__warm_start | False |
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| knn__algorithm | auto |
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| knn__leaf_size | 30 |
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| knn__metric | minkowski |
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| knn__metric_params | |
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| knn__n_jobs | |
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| knn__n_neighbors | 5 |
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| knn__p | 2 |
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| knn__weights | uniform |
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| ada__algorithm | deprecated |
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| ada__estimator | |
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| ada__learning_rate | 1.0 |
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| ada__n_estimators | 50 |
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| ada__random_state | 12345 |
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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: #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;}
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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%;
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}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
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}#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;
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}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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clickable and can be expanded/collapsed.
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- Pipeline and ColumnTransformer use this feature and define the default style
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- Estimators will overwrite some part of the style using the `sk-estimator` class
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*//* 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);
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}/* Toggleable label */
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#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;
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}#sk-container-id-5 label.sk-toggleable__label .caption {font-size: 0.6rem;font-weight: lighter;color: var(--sklearn-color-text-muted);
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}#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);
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}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
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}/* 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);
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}#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);
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}#sk-container-id-5 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
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}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
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}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
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}/* 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);
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}#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);
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}/* Estimator-specific style *//* Colorize estimator box */
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#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);
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}#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);
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}#sk-container-id-5 div.sk-label label.sk-toggleable__label,
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#sk-container-id-5 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
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}/* On hover, darken the color of the background */
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#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);
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}/* Label box, darken color on hover, fitted */
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#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);
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}/* Estimator label */#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
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}#sk-container-id-5 div.sk-label-container {text-align: center;
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}/* Estimator-specific */
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#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);
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}#sk-container-id-5 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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}/* on hover */
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#sk-container-id-5 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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}#sk-container-id-5 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
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}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
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a:link.sk-estimator-doc-link,
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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);
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}.sk-estimator-doc-link.fitted,
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a:link.sk-estimator-doc-link.fitted,
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a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
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}/* On hover */
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div.sk-estimator:hover .sk-estimator-doc-link:hover,
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.sk-estimator-doc-link:hover,
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div.sk-label-container:hover .sk-estimator-doc-link:hover,
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.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
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}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
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.sk-estimator-doc-link.fitted:hover,
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div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
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.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
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}/* Span, style for the box shown on hovering the info icon */
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.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);
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}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
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}.sk-estimator-doc-link:hover span {display: block;
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}/* "?"-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;
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}#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);
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}/* On hover */
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#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;
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}#sk-container-id-5 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
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}
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</style><div id="sk-container-id-5" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>VotingClassifier(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))])</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=[('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))])</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>
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## Evaluation Results
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[More Information Needed]
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# How to Get Started with the Model
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[More Information Needed]
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# Model Card Authors
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This model card is written by following authors:
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[More Information Needed]
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# Model Card Contact
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You can contact the model card authors through following channels:
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[More Information Needed]
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# Citation
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Below you can find information related to citation.
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**BibTeX:**
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```
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[More Information Needed]
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```
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# citation_bibtex
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to be done
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# get_started_code
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None
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# model_card_authors
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Syreeta, Shraddha, Sravani, Sadhana, Ranjitha
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# limitations
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Not handling logs
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# model_description
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Failure prediction and remediation
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