Python AUC - ROC Curve

Last modified on: October 20, 2024

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<h3><strong>AUC - ROC Curve in Python</strong></h3>
<p>The <strong>AUC - ROC Curve</strong> (Area Under the Receiver Operating Characteristic Curve) is a performance measurement for classification models at various threshold settings. It illustrates the trade-off between sensitivity (true positive rate) and specificity (1 - false positive rate) across different classification thresholds.</p>
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<h3><strong>Key Concepts of AUC - ROC Curve</strong></h3>
<ul>
<li><strong>ROC Curve</strong>: A plot that shows the true positive rate against the false positive rate for different threshold values.</li>
<li><strong>AUC</strong>: The area under the ROC curve. It provides a single value that summarizes the model's ability to discriminate between positive and negative classes. AUC values range from 0 to 1:
<ul>
<li>0.5 indicates no discrimination (random guessing).</li>
<li>1.0 indicates perfect discrimination.</li>
</ul>
</li>
</ul>
<h3><strong>Advantages of AUC - ROC</strong></h3>
<ul>
<li>Provides a visual representation of model performance.</li>
<li>Useful for imbalanced datasets, as it focuses on the rank order of predictions.</li>
</ul>
<h3><strong>Disadvantages of AUC - ROC</strong></h3>
<ul>
<li>Can be misleading for highly imbalanced datasets if not interpreted correctly.</li>
</ul>
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<h3><strong>Tutorial: Plotting AUC - ROC Curve in Python</strong></h3>
<p>We will use Python&rsquo;s <code>scikit-learn</code> library to demonstrate how to create and interpret the AUC - ROC curve for a binary classification problem.</p>
<h3><strong>1. Import Required Libraries</strong></h3>
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<pre class="language-python"><code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, roc_auc_score</code></pre>
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<h3><strong>2. Preparing the Data</strong></h3>
<p>For this example, we will use the <strong>Iris dataset</strong>, but we will focus on a binary classification problem (e.g., classifying one species versus the others).</p>
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<pre class="language-python"><code># Load the Iris dataset
iris = load_iris()
X = iris.data  # Features
y = (iris.target == 0).astype(int)  # Binary target (1 if species is 'setosa', else 0)

# Split the dataset into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)</code></pre>
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<h3><strong>3. Training a Model</strong></h3>
<p>We will train a <strong>Logistic Regression</strong> model for this binary classification task.</p>
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<pre class="language-python"><code># Initialize and fit the Logistic Regression model
model = LogisticRegression()
model.fit(X_train, y_train)</code></pre>
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<h3><strong>4. Generating Predictions and ROC Curve</strong></h3>
<p>We will generate predictions and compute the ROC curve.</p>
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<pre class="language-python"><code># Get predicted probabilities
y_scores = model.predict_proba(X_test)[:, 1]  # Probability of the positive class

# Calculate ROC curve
fpr, tpr, thresholds = roc_curve(y_test, y_scores)

# Calculate AUC
roc_auc = roc_auc_score(y_test, y_scores)
print(f'AUC: {roc_auc:.2f}')</code></pre>
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<h3><strong>5. Plotting the ROC Curve</strong></h3>
<p>Now, we can plot the ROC curve.</p>
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<pre class="language-python"><code># Plotting the ROC Curve
plt.figure(figsize=(10, 6))
plt.plot(fpr, tpr, color='blue', label=f'ROC Curve (AUC = {roc_auc:.2f})')
plt.plot([0, 1], [0, 1], color='red', linestyle='--')  # Diagonal line
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()
plt.grid()
plt.show()</code></pre>
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<h3><strong>Conclusion</strong></h3>
<p>In this tutorial, we:</p>
<ol>
<li>Introduced the <strong>AUC - ROC Curve</strong> as a performance metric for binary classification models.</li>
<li>Used the <strong>Iris dataset</strong> to demonstrate how to create and interpret the ROC curve.</li>
<li>Calculated the AUC value and visualized the ROC curve.</li>
</ol>
<p>The AUC - ROC curve is a powerful tool for evaluating the performance of classification models, especially when dealing with imbalanced datasets. It provides a clear understanding of how well the model can distinguish between different classes.</p>
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