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# Model description
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}#sk-container-id-4
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}#sk-container-id-4
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#sk-container-id-4 div.sk-
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#sk-container-id-4 div.sk-
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}.sk-
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}/*
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div.sk-
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}
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}/*
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}/*
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#sk-container-id-4
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}.
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}
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}
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}
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}.estimator
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}.
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# Model description
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This model is a Gaussian Naive Bayes classifier trained on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. It predicts whether a breast tumor is malignant (cancerous) or benign (non-cancerous) based on 30 numerical features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.
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The model uses Gaussian Naive Bayes because the features are continuous and approximately normally distributed after scaling. PCA was also used to visualize structure and variance in the dataset, confirming that classes were fairly separable in reduced dimensions.
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## Intended uses & limitations
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**Intended use:**
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This model is for **educational and research purposes only**. It demonstrates binary classification, feature scaling, and probabilistic modeling using Gaussian Naive Bayes.
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**Not for clinical or real-world use.**
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The dataset comes from a medical diagnostic context, but this model has not been clinically validated or certified for healthcare applications.
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**Limitations:**
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- Naive Bayes assumes feature independence, which may not hold perfectly in real data.
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- Performance depends heavily on data quality and class balance.
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- Should not be used to make real patient predictions or medical decisions.
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## Training Procedure
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**Training data:**
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Wisconsin Diagnostic Breast Cancer dataset, containing 569 samples and 30 numeric features (radius, texture, area, smoothness, etc.).
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**Preprocessing:**
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- Features were standardized using `StandardScaler`.
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- PCA (2 components) was used for visualization only, not for model fitting.
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**Training/testing split:**
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- 80% training / 20% testing (stratified)
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- The training data was further split into subtrain/validation (75%/20% of total).
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**Model:**
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- Gaussian Naive Bayes (default parameters)
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- Fitted on scaled features.
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**Evaluation metrics:**
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- Confusion matrix and classification report were generated.
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- The model achieved high precision and recall, indicating good generalization on the test set.
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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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| priors | None |
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| var_smoothing | 1e-09 |
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</details>
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### Model Plot
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<style>#sk-container-id-4 {/* 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-4 {color: var(--sklearn-color-text);
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}#sk-container-id-4 pre {padding: 0;
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}#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;
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}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
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}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
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}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;
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}/* Serial-specific style estimator block */#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
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}/* Toggleable content - dropdown */#sk-container-id-4 div.sk-toggleable__content {display: none;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
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}#sk-container-id-4 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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}#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
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}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */display: block;width: 100%;overflow: visible;
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}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
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}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,
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#sk-container-id-4 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-4 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-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
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}#sk-container-id-4 div.sk-label-container {text-align: center;
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}/* Estimator-specific */
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#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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}#sk-container-id-4 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-4 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-4 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-4 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-4 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
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}.estimator-table summary {padding: .5rem;font-family: monospace;cursor: pointer;
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}.estimator-table details[open] {padding-left: 0.1rem;padding-right: 0.1rem;padding-bottom: 0.3rem;
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}.estimator-table .parameters-table {margin-left: auto !important;margin-right: auto !important;
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}.estimator-table .parameters-table tr:nth-child(odd) {background-color: #fff;
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}.estimator-table .parameters-table tr:nth-child(even) {background-color: #f6f6f6;
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}.estimator-table .parameters-table tr:hover {background-color: #e0e0e0;
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}.estimator-table table td {border: 1px solid rgba(106, 105, 104, 0.232);
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}.user-set td {color:rgb(255, 94, 0);text-align: left;
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}.user-set td.value pre {color:rgb(255, 94, 0) !important;background-color: transparent !important;
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}.default td {color: black;text-align: left;
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}.user-set td i,
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.default td i {color: black;
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}.copy-paste-icon {background-image: url(data:image/svg+xml;base64,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);background-repeat: no-repeat;background-size: 14px 14px;background-position: 0;display: inline-block;width: 14px;height: 14px;cursor: pointer;
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}
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</style><body><div id="sk-container-id-4" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" checked><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianNB</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.naive_bayes.GaussianNB.html">?<span>Documentation for GaussianNB</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix=""><div class="estimator-table"><details><summary>Parameters</summary><table class="parameters-table"><tbody><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('priors',this.parentElement.nextElementSibling)"></i></td><td class="param">priors </td><td class="value">None</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('var_smoothing',this.parentElement.nextElementSibling)"></i></td><td class="param">var_smoothing </td><td class="value">1e-09</td></tr></tbody></table></details></div></div></div></div></div></div><script>function copyToClipboard(text, element) {// Get the parameter prefix from the closest toggleable contentconst toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;const originalStyle = element.style;const computedStyle = window.getComputedStyle(element);const originalWidth = computedStyle.width;const originalHTML = element.innerHTML.replace('Copied!', '');navigator.clipboard.writeText(fullParamName).then(() => {element.style.width = originalWidth;element.style.color = 'green';element.innerHTML = "Copied!";setTimeout(() => {element.innerHTML = originalHTML;element.style = originalStyle;}, 2000);}).catch(err => {console.error('Failed to copy:', err);element.style.color = 'red';element.innerHTML = "Failed!";setTimeout(() => {element.innerHTML = originalHTML;element.style = originalStyle;}, 2000);});return false;
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}document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {const toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const paramName = element.parentElement.nextElementSibling.textContent.trim();const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;element.setAttribute('title', fullParamName);
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});
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</script></body>
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## Evaluation Results
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[More Information Needed]
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|
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# How to Get Started with the Model
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|
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[More Information Needed]
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# Model Card Authors
|
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|
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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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+
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# Citation
|
| 174 |
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|
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Below you can find information related to citation.
|
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**BibTeX:**
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| 178 |
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```
|
| 179 |
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[More Information Needed]
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| 180 |
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```
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| 181 |
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# Model Card Author
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|
| 184 |
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Jad Alsassa
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# Intended Uses & Limitations
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This model is for educational and demonstration purposes only. It is not validated for medical or clinical use.
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# Model Description
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This Gaussian Naive Bayes model predicts breast tumor malignancy using the Wisconsin Diagnostic Breast Cancer dataset. Features were standardized and PCA was applied for visualization but not during training.
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