Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Partial AUC Scores: A Better Metric for Binary Classification

ssawla
ssawla Last Updated : 16 Feb, 2023
5 min read

Introduction

Partial AUC (Area Under the Curve) scores are a valuable tool for evaluating the performance of binary classification models, particularly when the class distribution is highly imbalanced. Unlike traditional AUC scores, partial AUC scores concentrate on a specific region of the ROC (Receiver Operating Characteristic) curve, offering a more detailed evaluation of the model’s performance.

"partial AUC scores

This blog post will dive into what partial AUC scores are, how they are calculated, and why they are essential for evaluating imbalanced datasets. We will also include relevant examples and a code example using Python to help make these concepts clearer.

Learning Objectives:

  1. Learning the basics of AUC Scores.
  2. What are the disadvantages of AUC Scores, and how to overcome them?
  3. How to calculate partial scores and their objective.

This article was published as a part of the Data Science Blogathon.

Table of Contents

What do AUC Scores Mean?

AUC (Area Under the Curve) scores are a commonly used metric for evaluating the performance of binary classification models. The traditional AUC score calculates the area under the ROC (Receiver Operating Characteristic) curve, which plots the True Positive Rate (TPR) against the False Positive Rate (FPR) for all possible threshold values. The score ranges from 0.5 for a random model to 1 for a perfect model, with values closer to 1 indicating better performance.

"partial AUC scores

The Drawback of AUC Scores

However, in real-world applications, the class distribution of the target variable can be highly imbalanced, meaning that one class is much more prevalent than the other. In these cases, the traditional AUC score may not provide a good evaluation of the model’s performance as it aggregates the performance overall threshold values and does not account for the imbalance in the class distribution.

Overcoming the Drawback

This is where partial AUC scores come into play. Unlike traditional AUC scores, they focus on a specific region of the ROC curve, providing a more granular evaluation of the model’s performance. This allows for a more accurate evaluation of the model’s performance, especially in cases where the class distribution is highly imbalanced.

For example, in a fraud detection problem, the partial AUC score can be calculated for the region where the FPR is less than a specific value, such as 0.05. This provides an evaluation of the model’s performance at catching fraud instances while ignoring the performance on the majority class instances. This information can be used to make informed decisions about which models to use, how to improve models, and how to adjust the threshold values for predictions.

Calculating Partial AUC Scores

Calculating partial AUC scores involves dividing the ROC curve into intervals and then computing the AUC for each interval. The intervals can be defined in terms of the FPR or TPR, and the size of the intervals can be adjusted to control the granularity of the evaluation. The partial AUC score for a specific interval is calculated as the sum of the areas of the rectangles formed by the interval boundaries and the ROC curve within that interval.

For example, to calculate the partial AUC score for the region where the FPR is less than 0.05,

  1. we would first divide the ROC curve into intervals for the specific interval where the FPR is less than 0.05.
  2. Then, we would calculate the sum of the areas of the rectangles formed by the interval boundaries and the ROC curve within that interval. This would give us the partial AUC score for that region.

In addition to the fraud detection example, partial AUC scores can be used in a variety of other real-world applications such as medical diagnosis, credit scoring, and marketing.

  1. In medical diagnosis, they can be used to evaluate the performance of a model in detecting a specific disease while ignoring its performance on healthy individuals.
  2. In credit scoring, they can be used to evaluate the performance of a model in detecting high-risk loan applicants while ignoring its performance on low-risk loan applicants.
  3. In marketing, they can be used to evaluate the performance of a model in predicting which customers are most likely to purchase while ignoring its performance on customers who are unlikely to make a purchase.

In conclusion, partial AUC scores are an important tool for evaluating the performance of binary classification models, especially in cases where the class distribution is highly imbalanced. By focusing on a specific region of the ROC curve, partial AUC scores provide a more granular evaluation of the model’s performance, which can be used to make informed decisions about model selection, improvement, and threshold adjustment. Understanding them and how to use them is an important part of the evaluation process for binary classification models and can lead to more accurate and effective decision-making in various real-world applications.

It’s important to note that partial AUC scores are not a replacement for traditional AUC scores but rather a complementary tool to be used in conjunction with traditional AUC scores. While they provide a more nuanced evaluation of the model’s performance in specific regions of the ROC curve, traditional AUC scores provide a more holistic evaluation of the model’s overall performance.

When evaluating binary classification models, it’s best to use both traditional AUC scores and partial AUC scores to get a complete picture of the model’s performance. This can be done by plotting the ROC curve and calculating both the traditional AUC score and the partial AUC scores for specific regions of the curve.

Partial AUC Scores in Python

Now, let’s see how to calculate partial AUC scores in Python. The easiest way to calculate partial AUC scores in Python is by using the “roc_auc_score” function from the scikit-learn library. This function calculates the traditional AUC score by default, but it can also be used to calculate partial AUC scores by passing in the “curve” parameter.

For example, let’s say we have a binary classification model and its predictions on the test data. We can use the following code to calculate the traditional AUC score:

from sklearn.metrics import roc_auc_score

y_true = [0, 0, 1, 1]

y_scores = [0.1, 0.4, 0.35, 0.8]

auc = roc_auc_score(y_true, y_scores)

print('AUC:', auc)

To calculate the partial AUC score for the region where the FPR is less than 0.05, we can pass in the “max_fpr” parameter as follows

from sklearn.metrics import roc_auc_score
y_true = [0, 0, 1, 1]
y_scores = [0.1, 0.4, 0.35, 0.8]
auc = roc_auc_score(y_true, y_scores, max_fpr=0.05)
print('Partial AUC:', auc)

Conclusion

In summary, it provides a more granular evaluation of the performance of binary classification models, especially in cases where the class distribution is highly imbalanced. Understanding and using them can greatly enhance the evaluation of binary classification models in imbalanced datasets.

  • By combining traditional AUC and partial AUC scores, we can get a complete picture of the model’s performance and make informed decisions about model selection, improvement, and threshold adjustment.
  • With the help of Python’s scikit-learn library, calculating partial AUC scores is a straightforward and convenient process.
  • When evaluating binary classification models, it’s best to use both traditional AUC scores and partial AUC scores to get a complete picture of the model’s performance.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

ssawla
ssawla

Hi there! I'm an experienced data scientist with over 7 years under my hood. In my free time, I love sharing my knowledge by writing blogs and helping others understand the exciting world of data science. Let's connect and learn from each other!

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details