In the world of machine learning and deep learning, achieving high accuracy on a model's performance metrics is often considered the ultimate goal. However, it is also crucial to understand the scenarios in which the model underperforms, even when the overall accuracy is seemingly impressive. This is where failure mode analysis steps in, allowing us to identify the weaknesses and areas for improvement in our models. In this blog post, we will explore how the RagaAI Testing Platform leveraged failure mode analysis to detect model underperformance and enable iterative improvements.

1. CelebA-Spoof Dataset and the Model

To illustrate the practical application of failure mode analysis, let's delve into a real-world example. We will consider the CelebA-Spoof dataset, which aims to classify images as either fake or genuine. The dataset comprises a diverse range of images, including live images and various forms of spoofed images, such as printed photos, digital masks, and video replays. Our goal is to build a robust model capable of correctly classifying these images.

The model created for this task employs state-of-the-art techniques, leveraging deep neural networks and sophisticated architecture designs. The development process involved training the model on a large labelled dataset, performing rigorous validation, and fine-tuning the model to achieve high accuracy.

2. Introduction to RagaAI Testing Platform

To thoroughly evaluate the model's performance and identify potential failure modes, we utilized the RagaAI Testing Platform. RagaAI offers a comprehensive suite of testing tools specifically designed for machine learning models. The platform provides various testing methodologies, such as benchmarking, fairness evaluation, adversarial attacks, and failure mode analysis.

3. Failure Mode Analysis with RagaAI Testing Platform

Failure mode analysis is a critical component of evaluating model performance. It helps uncover scenarios in which the model struggles or fails to perform optimally, despite an overall high accuracy. By identifying these failure modes, we can focus on improving the model's performance in specific and important scenarios.

The RagaAI Testing Platform uses a combination of techniques, including automated testing scripts, statistical analysis, and anomaly detection algorithms, to pinpoint failure modes in machine learning models. It analyzes the dataset to find scenarios where the model exhibits inconsistencies or subpar predictions.

4. Example Result

To provide a clearer illustration, let's take an example scenario from the CelebA-Spoof dataset. In this case, an image with a printed photo on a mask was misclassified as a genuine image by the model. The failure mode analysis accurately identified this specific failure mode, highlighting the need for improvements in handling images with artificial masks.

Model performance on the overall dataset

On the celebA-Spoof dataset model’s overall performance was a precision score of 0.73 as highlighted in the below image

Model under-performance on a specific cluster of dataset

In cluster 8, the model has zero precision signifying that there are edge cases in the dataset where the model is underperforming and in the data grid we can see that these cases are mostly where images have masks to fool the system. Hence, data scientist needs to resolve model underperformance in these specific edge cases

5. Enabling Model Improvement

The insights gained through failure mode analysis with the RagaAI Testing Platform offered invaluable guidance for improving the model's performance. Armed with the knowledge of the specific failure mode, we could focus on enhancing the model's ability to accurately classify images with artificial masks.

This iterative improvement process involved augmentation techniques, additional training data, and fine-tuning the model's architecture. The RagaAI Testing Platform acted as a reliable testing ground to assess the effectiveness of these improvements, continually validating the model's performance.