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 […]
Fine Tuning Model Evaluation using ROC and Precision Recall curves
Insights and strategies on testing Machine Learning Models
Once a machine learning model is developed and its accuracy and related metrics have been thoroughly examined, it might seem like the model is ready for real-world deployment. However in reality this is hardly the case. Major part of testing begins when the model is integrated into the application it was designed for. We at Qxf2 Services feel most of […]
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 […]
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 […]
Baseline Model Comparison for Performance Evaluation
Machine learning models evolve. As a tester, how do we know the newer version of the model is better? How do we know that the model did not get worse in other areas? The most intuitive approach would be to design a ‘good’ labelled dataset and then calculate the evaluation score like the F1 score for the model under test. […]
Testing OpenAI Whisper with Indian Languages
In previous blog, we tested OpenAI Whisper for English language with different accents and observed it did great job. We also provided details about how we generated audios, setup and test details. In this blog, we attempted to test OpenAI Whisper’s capability to transcribe and translate Indian Languages. At Qxf2, our teammates work from different regions of India, and everyone […]
Testing OpenAI Whisper with different accents
At Qxf2, we did some black box testing on OpenAI Whisper – a tool that does speech recognition well. OpenAI Whisper is also capable of language detection and translation. This model can be tested in various ways, by adjusting different voice attributes such as volume, pace, pitch, rate, etc. However, in this particular case, we have chosen to test it […]