Improving Data Quality: Oversampling to Address Imbalanced Dataset

The importance of data quality in model training cannot be overstated. A high-quality, well-balanced dataset is the foundation upon which effective models are built. Without it, even the most sophisticated algorithms can produce unreliable or biased predictions. Before jumping into testing a model, it is crucial for data scientists and testers alike to have a clear understanding of the data […]

Metamorphic testing with SHAP Analysis

Artificial Intelligence and Machine Learning (AI/ML) have become a part of everything we do in our daily work. From personalised recommendations to automated decision-making, these technologies are everywhere. As AI/ML systems become more advanced, it’s crucial to ensure they are reliable and accurate. In this blog, we’ll explore simple and effective testing strategies to help improve these products, making them […]

Fine Tuning Model Evaluation using ROC and Precision Recall curves

Evaluating machine learning models is crucial for understanding their performance characteristics. In this blog post, we explore how ROC and Precision Recall curves can be used to improve the way we evaluate models. Additionally, we delve into the practical aspect of using these curves across various thresholds, customizing the model for specific requirements and achieving optimal performance. Why this post […]

Understanding Text Classification Models with LIME

Why this post? I’ve always been wondering how Machine learning models functions as black boxes, making predictions based on patterns learned from data. Despite the impressive accuracy, understanding the factors and features that influenced a particular prediction and the decision-making process is crucial and challenging task. The lack of transparency in these models adds complexity making their internal workings less […]

Data quality matters when building and refining a Classification Model

In the world of machine learning, data takes centre stage. It’s often said that data is the key to success. In this blog post, we emphasise the significance of data, especially when building a comment classification model. We will delve into how data quality, quantity, and biases significantly influence machine learning model performance. Additionally, we’ll explore techniques like undersampling as […]

Build a semantic search tool using FAISS

This post provides an overview of implementing semantic search. Why? Because too often, we notice testers skip testing more complex features like autocomplete. This might be ok in most applications. But in domain specific applications, testing autocomplete capabilities of the product is important. Since testers can benefit from understanding implementation details, in this post, we will look at how autocomplete […]

Robustness Testing of Machine Learning Models

In the world of machine learning, assessing a model’s performance under real-world conditions is important to ensure its reliability and robustness. Real-world data is usually not perfect, it may contain messy data or data with noise, outliers, and variations. During model training, these types of data could be limited, and the model may not have received sufficient training to handle […]