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---
license: mit
library_name: sklearn
tags:
- classification
- phishing
---
# Model description
## Training Procedure
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-------------------------------------------------------------|--------------------------|
| memory | |
| steps | [('standardscaler', StandardScaler()), ('calibratedclassifiercv', CalibratedClassifierCV(cv=5, estimator=RandomForestClassifier(),<br /> method='isotonic'))] |
| verbose | False |
| standardscaler | StandardScaler() |
| calibratedclassifiercv | CalibratedClassifierCV(cv=5, estimator=RandomForestClassifier(),<br /> method='isotonic') |
| standardscaler__copy | True |
| standardscaler__with_mean | True |
| standardscaler__with_std | True |
| calibratedclassifiercv__base_estimator | deprecated |
| calibratedclassifiercv__cv | 5 |
| calibratedclassifiercv__ensemble | True |
| calibratedclassifiercv__estimator__bootstrap | True |
| calibratedclassifiercv__estimator__ccp_alpha | 0.0 |
| calibratedclassifiercv__estimator__class_weight | |
| calibratedclassifiercv__estimator__criterion | gini |
| calibratedclassifiercv__estimator__max_depth | |
| calibratedclassifiercv__estimator__max_features | sqrt |
| calibratedclassifiercv__estimator__max_leaf_nodes | |
| calibratedclassifiercv__estimator__max_samples | |
| calibratedclassifiercv__estimator__min_impurity_decrease | 0.0 |
| calibratedclassifiercv__estimator__min_samples_leaf | 1 |
| calibratedclassifiercv__estimator__min_samples_split | 2 |
| calibratedclassifiercv__estimator__min_weight_fraction_leaf | 0.0 |
| calibratedclassifiercv__estimator__n_estimators | 100 |
| calibratedclassifiercv__estimator__n_jobs | |
| calibratedclassifiercv__estimator__oob_score | False |
| calibratedclassifiercv__estimator__random_state | |
| calibratedclassifiercv__estimator__verbose | 0 |
| calibratedclassifiercv__estimator__warm_start | False |
| calibratedclassifiercv__estimator | RandomForestClassifier() |
| calibratedclassifiercv__method | isotonic |
| calibratedclassifiercv__n_jobs | |
</details>
### Model Plot
This is the architecture of the model loaded by joblib.
<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#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-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#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;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#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;}#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 the default 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;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('standardscaler', StandardScaler()),('calibratedclassifiercv',CalibratedClassifierCV(cv=5,estimator=RandomForestClassifier(),method='isotonic'))])</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('standardscaler', StandardScaler()),('calibratedclassifiercv',CalibratedClassifierCV(cv=5,estimator=RandomForestClassifier(),method='isotonic'))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">calibratedclassifiercv: CalibratedClassifierCV</label><div class="sk-toggleable__content"><pre>CalibratedClassifierCV(cv=5, estimator=RandomForestClassifier(),method='isotonic')</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier()</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>
## Evaluation Results
| Metric | Value |
|-----------|----------|
| accuracy | 0.945652 |
| f1-score | 0.945114 |
| precision | 0.951996 |
| recall | 0.938331 |
## Test Report
## Model Interpretation
### Feature Importances
# How to Get Started with the Model
Below are some code snippets to load the model.
## With joblib (not recommended)
```python
import joblib
import pandas as pd
urls = [
{
"url": "https://www.rga.com/about/workplace",
"nb_hyperlinks": 97.0,
"ratio_intHyperlinks": 0.969072165,
"ratio_extHyperlinks": 0.030927835,
"ratio_extRedirection": 0.0,
"safe_anchor": 25.0,
"domain_registration_length": 3571.0,
"domain_age": 11039,
"web_traffic": 178542.0,
"google_index": 0.0,
"page_rank": 5,
},
]
model = joblib.load("models/model.pkl")
df = pd.DataFrame(urls)
df = df.set_index("url")
probas = model.predict_proba(df.values)
for url, proba in zip(urls, probas):
print(f"URL: {url['url']}")
print(f"Likelihood of being a phishing site: {proba[1] * 100:.2f}%")
print("----")
# output:
# URL: https://www.rga.com/about/workplace
# Likelihood of being a phishing site: 0.89%
# ----
```
## With ONNX (recommended)
### Python
```python
import numpy as np
import onnxruntime
import pandas as pd
# Defining a list of URLs with characteristics
urls = [
{
"url": "https://www.rga.com/about/workplace",
"nb_hyperlinks": 97.0,
"ratio_intHyperlinks": 0.969072165,
"ratio_extHyperlinks": 0.030927835,
"ratio_extRedirection": 0.0,
"safe_anchor": 25.0,
"domain_registration_length": 3571.0,
"domain_age": 11039,
"web_traffic": 178542.0,
"google_index": 0.0,
"page_rank": 5,
},
]
# Initializing the ONNX Runtime session with the pre-trained model
sess = onnxruntime.InferenceSession(
"models/model.onnx",
providers=["CPUExecutionProvider"],
)
# Creating a DataFrame from the list of URLs
df = pd.DataFrame(urls)
df = df.set_index("url")
# Converting DataFrame data to a float32 NumPy array
inputs = df.astype(np.float32).to_numpy()
# Using the ONNX model to make predictions on the input data
probas = sess.run(None, {"X": inputs})[1]
# Displaying the results
for url, proba in zip(urls, probas):
print(proba)
print(f"URL: {url['url']}")
print(f"Likelihood of being a phishing site: {proba[1] * 100:.2f}%")
print("----")
# output:
# URL: https://www.rga.com/about/workplace
# Likelihood of being a phishing site: 0.89%
# ----
```
### JavaScript
```javascript
const ort = require('onnxruntime-node');
const urls = [
{
"url": "http://rapidpaws.com/wp-content/we_transfer/index2.php?email=/",
"nb_hyperlinks": 1,
"ratio_intHyperlinks": 1,
"ratio_extHyperlinks": 0,
"ratio_extRedirection": 0,
"safe_anchor": 0,
"domain_registration_length": 338,
"domain_age": 0,
"web_traffic":1853,
"google_index": 1,
"page_rank": 2,
},
];
async function main() {
try {
// Creating an ONNX inference session with the specified model
const model_path = "./models/model.onnx";
const session = await ort.InferenceSession.create(model_path);
// Get values from data and remove url links
const inputs = urls.map(url => Object.values(url).slice(1));
// Flattening the 2D array to get a 1D array
const flattenInputs = inputs.flat();
// Creating an ONNX tensor from the input array
const tensor = new ort.Tensor('float32', flattenInputs, [inputs.length, 10]);
// Executing the inference session with the input tensor
const results = await session.run({"X": tensor});
// Retrieving probability data from the results
const probas = results['probabilities'].data;
// Displaying results for each URL
urls.forEach((url, index) => {
// The index * 2 + 1 is used to access the probability associated with the phishing class
const proba = probas[index * 2 + 1];
const percent = (proba * 100).toFixed(2);
console.log(`URL: ${url.url}`);
console.log(`Likelihood of being a phishing site: ${percent}%`);
console.log("----");
});
} catch (e) {
console.log(`failed to inference ONNX model: ${e}.`);
}
};
main();
// output:
// URL: https://www.rga.com/about/workplace
// Likelihood of being a phishing site: 0.89%
// ----
```
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