| # Model description | |
| This is a Decision tree model trained on a phishing URL detection dataset. The dataset contains features from URLs and their webpage content. The model is trained to classify whether a website is legitimate (label 1) or an act of phishing (label 0). | |
| ## Intended uses & limitations | |
| This model is made for educational purposes and is not ready to be used in production. | |
| ## Training Procedure | |
| With this dataset, I will be using a decision tree to predict whether certain websites are legitimate or an act of phishing. This is because Naive Bayes assumes feature independence, which is not true for this case. Decision trees split data based on actual patterns, which is useful for phishing detection. | |
| Overall, the data does not meet the criteria for Naive Bayes because the features are not independent. | |
| Using decision trees for this case is crucial because it offers clear interpretability of the classification logic. It will be able to differentiate legitimate vs illegitimate websites. | |
| ## How to use the model | |
| In your notebook, paste the following code: | |
| ------------------------------------------------------------- | |
| from huggingface_hub import hf_hub_download | |
| hf_hub_download(repo_id="CSC310-fall25/training_classification_phishing", filename="model.pkl",local_dir='.') | |
| dt_loaded = sio.load('model.pkl') | |
| ------------------------------------------------------------- | |
| This will load the model. | |
| You can download the appropriate test data by pasting this code: | |
| ------------------------------------------------------------- | |
| hf_hub_download(repo_id="CSC310-fall25/training_classification_phishing", filename="phishing_test.csv",local_dir='.') | |
| phishing_test = pd.read_csv('phishing_test.csv') | |
| ------------------------------------------------------------- | |
| ## Dataset Details | |
| Dataset Characteristics: Tabular | |
| Associated Tasks: Classification | |
| Number of Features: 54 | |
| Number of Instances: 235795 | |
| Feature Type: Real, Categorical, Integer | |
| ### Hyperparameters | |
| <details> | |
| <summary> Click to expand </summary> | |
| | Hyperparameter | Value | | |
| | :----------------------: | :----: | | |
| | ccp_alpha | 0.0 | | |
| | class_weight | None | | |
| | criterion | gini | | |
| | max_depth | 2 | | |
| | max_features | None | | |
| | max_leaf_nodes | None | | |
| | min_impurity_decrease | 0.0 | | |
| | min_samples_leaf | 0.2 | | |
| | min_samples_split | 0.2 | | |
| | min_weight_fraction_leaf | 0.0 | | |
| | monotonic_cst | None | | |
| | random_state | 67 | | |
| | splitter | random | | |
| </details> | |
| ### Model Plot | |
| <style>#sk-container-id-2 {/* 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-2 {color: var(--sklearn-color-text); | |
| }#sk-container-id-2 pre {padding: 0; | |
| }#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px; | |
| }#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background); | |
| }#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative; | |
| }#sk-container-id-2 div.sk-text-repr-fallback {display: none; | |
| }div.sk-parallel-item, | |
| div.sk-serial, | |
| div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center; | |
| }/* Parallel-specific style estimator block */#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1; | |
| }#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; | |
| }#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column; | |
| }#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%; | |
| }#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%; | |
| }#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0; | |
| }/* Serial-specific style estimator block */#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em; | |
| }/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is | |
| clickable and can be expanded/collapsed. | |
| - Pipeline and ColumnTransformer use this feature and define the default style | |
| - Estimators will overwrite some part of the style using the `sk-estimator` class | |
| *//* Pipeline and ColumnTransformer style (default) */#sk-container-id-2 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background); | |
| }/* Toggleable label */ | |
| #sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: 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-2 label.sk-toggleable__label .caption {font-size: 0.6rem;font-weight: lighter;color: var(--sklearn-color-text-muted); | |
| }#sk-container-id-2 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon); | |
| }#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); | |
| }/* Toggleable content - dropdown */#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); | |
| }#sk-container-id-2 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); | |
| }#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); | |
| }#sk-container-id-2 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0); | |
| }#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto; | |
| }#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; | |
| }/* Pipeline/ColumnTransformer-specific style */#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); | |
| }#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2); | |
| }/* Estimator-specific style *//* Colorize estimator box */ | |
| #sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); | |
| }#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2); | |
| }#sk-container-id-2 div.sk-label label.sk-toggleable__label, | |
| #sk-container-id-2 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background); | |
| }/* On hover, darken the color of the background */ | |
| #sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); | |
| }/* Label box, darken color on hover, fitted */ | |
| #sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2); | |
| }/* Estimator label */#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em; | |
| }#sk-container-id-2 div.sk-label-container {text-align: center; | |
| }/* Estimator-specific */ | |
| #sk-container-id-2 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); | |
| }#sk-container-id-2 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); | |
| }/* on hover */ | |
| #sk-container-id-2 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); | |
| }#sk-container-id-2 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2); | |
| }/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link, | |
| a:link.sk-estimator-doc-link, | |
| a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 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-2 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid; | |
| }#sk-container-id-2 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); | |
| }/* On hover */ | |
| #sk-container-id-2 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; | |
| }#sk-container-id-2 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); | |
| } | |
| </style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier(max_depth=2, min_samples_leaf=0.2, min_samples_split=0.2,random_state=67, splitter='random')</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-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>DecisionTreeClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html">?<span>Documentation for DecisionTreeClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>DecisionTreeClassifier(max_depth=2, min_samples_leaf=0.2, min_samples_split=0.2,random_state=67, splitter='random')</pre></div> </div></div></div></div> | |
| ## Evaluation Results | |
| precision recall f1-score support | |
| 0 1.00 0.74 0.85 20189 | |
| 1 0.84 1.00 0.91 26970 | |
| accuracy 0.89 47159 | |
| macro avg 0.92 0.87 0.88 47159 | |
| weighted avg 0.91 0.89 0.88 47159 | |
| The overall accuracy of the model was 89%. For Class 0, the precision score was 100%, meaning it was able to predict every phishing website correctly. Its recall score was 74%, meaning it missed about 26% of actual phishing samples. For Class 1, its precision score was 84% and its recall score was 100%. This shows that the model leans toward predicting '1,' while favoring recall rather than precision. | |
| The model could possibly be used for real scenarios, but not for high risk use. It would be helpful in situations where you only need a general screening of phishing vs legitimate websites. So although it is accurate, it is not accurate enough. However, I would still trust this model. It performs well and behaves predictably. If more precision/recall is needed, then it would certainly make sense to use a more complex model. It all depends on what the model is being used for. | |
| ## Visualization | |
| plt.figure(figsize=(15,20)) | |
| tree.plot_tree(dt, rounded =True, class_names = ['0','1'], | |
| proportion=True, filled =True, impurity=False,fontsize=10); | |
|  | |
| # Model Card Authors | |
| Anthony Martinez | |
| # Model Card Contact | |
| You can contact the model card authors through following channels: anthony.martinez@uri.edu | |
| # Citation | |
| Dataset: https://archive.ics.uci.edu/dataset/967/phiusiil+phishing+url+dataset | |