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. […]
My experience with Auto-GPT
Qxf2 was intrigued by the rising trend of LLMs. We decided to venture beyond ChatGPT. With Auto-GPT’s increasing popularity and the widespread claims, we were eager to explore its capabilities. Given my background as an engineer, I was particularly equipped to dive into the intricacies of Auto-GPT. And as curious tester, I wanted to get a sense of how we […]
Generate CPU load using Python
Problem: How do I spike up my CPU load? Recently at one of our client engagement, for one of our tests, we needed to spike up CPU usage in a controlled manner on some of our Unix servers. One way to do this was to generate a specific amount of load for a specific amount of time. This was a […]
Saving cProfile stats to a csv file
As part of testing one of my client code, I used cProfile to profile some code. Though I had never tried profiling code before, I found cProfile easy to use. However, I faced some challenges on how to use the stats which cProfile provided. I looked at ways to use the stats but I didn’t find an option where I […]
Python Trello API utility script
Python Trello API Util is a set of scripts to automate various Trello tasks like creating Trello Boards, assigning people to Board, creating cards, copying cads etc. It is built using Python Requests library and was part of a Hackathon held at Qxf2 Services. Why this post? In case you have used Trello, you will be familiar with the process […]
Qxf2’s automation testing framework uses Python 3
We have updated our open-sourced GUI automation testing framework to use Python3. This update was due for a long time. We have had a lot of requests from people asking for our framework to use Python3 and we finally found the bandwidth needed to migrate to Python3. We will also no longer support Python2. If you already use our framework […]