Why use Area under the curve? (AUC - ROC)
In scenarios with imbalanced datasets, ROC curves and AUC-ROC scores are valuable tools for assessing and comparing the performance of machine learning classifiers. They help provide insights into a model's ability to distinguish between classes and can guide decision-making regarding threshold selection.
Machine Learning Models Basic Performance Metrics
When analyzing data using ML, a suitable model is selected based on the task. Classifier models learn from labeled training data and predict discrete classes, while regression models learn from training data and predict continuous values. To evaluate the performance of machine learning models, various metrics are used. These include accuracy, precision, recall, F1 score, AUC-ROC, MAE, MSE, and R-squared. The choice of metrics depends on the specific problem and the nature of the data. Visualization tools such as confusion matrices, ROC curves, precision-recall curves and others can be used to gain insights into the performance of classifiers and understand their behavior. When dealing with imbalanced data, using accuracy as an evaluation metric can be misleading. Accuracy does not account for class imbalance, it may overestimate the performance. It is important to consider other metrics such as AUC and others which provide a more comprehensive evaluation performance in imbalanced datasets.
C to C++: 3 Proven Techniques for Embedded Systems Transformation
For 50 years, the C programming language has dominated the embedded software industry. Even today, more than 80% of embedded projects are using C; however, over the last few years, many teams have begun transitioning from C to C++. C++ offers...
3 Tips for using ChatGPT for Embedded Software
Unless you’ve been hiding under a rock, the internet has been ablaze with conversations, videos, and blogs about ChatGPT. ChatGPT is a chatbot that interacts with a user conversationally. The chatbot can answer questions, request clarification,...
TensorFlow Datasets
TensorFlow Datasets are commonly used for sharing datasets in the public domain. Well known examples include the MNIST dataset for classification and the OxfordIIITPET dataset for segmentation. This article explains how TensorFlow Datasets work...
How to Architect a TinyML Application with an RTOS
An interesting question I’ve been asked on several occasions is, “How do I use machine learning with an RTOS?”. As machine learning finds its way into more applications, there will be applications that target low-power, clock-limited, edge...
Is Machine Learning Ready for Microcontroller-based Systems?
“Machine learning” is currently technologies number one hype word. Mention machine learning and venture capitalists open their checkbooks with visions of riches and grandeur. Developers froth at the mouth with opportunities and dreams of...
3 Tips for using ChatGPT for Embedded Software
Unless you’ve been hiding under a rock, the internet has been ablaze with conversations, videos, and blogs about ChatGPT. ChatGPT is a chatbot that interacts with a user conversationally. The chatbot can answer questions, request clarification,...
Why use Area under the curve? (AUC - ROC)
In scenarios with imbalanced datasets, ROC curves and AUC-ROC scores are valuable tools for assessing and comparing the performance of machine learning classifiers. They help provide insights into a model's ability to distinguish between classes and can guide decision-making regarding threshold selection.
Machine Learning Models Basic Performance Metrics
When analyzing data using ML, a suitable model is selected based on the task. Classifier models learn from labeled training data and predict discrete classes, while regression models learn from training data and predict continuous values. To evaluate the performance of machine learning models, various metrics are used. These include accuracy, precision, recall, F1 score, AUC-ROC, MAE, MSE, and R-squared. The choice of metrics depends on the specific problem and the nature of the data. Visualization tools such as confusion matrices, ROC curves, precision-recall curves and others can be used to gain insights into the performance of classifiers and understand their behavior. When dealing with imbalanced data, using accuracy as an evaluation metric can be misleading. Accuracy does not account for class imbalance, it may overestimate the performance. It is important to consider other metrics such as AUC and others which provide a more comprehensive evaluation performance in imbalanced datasets.
C to C++: 3 Proven Techniques for Embedded Systems Transformation
For 50 years, the C programming language has dominated the embedded software industry. Even today, more than 80% of embedded projects are using C; however, over the last few years, many teams have begun transitioning from C to C++. C++ offers...
TensorFlow Datasets
TensorFlow Datasets are commonly used for sharing datasets in the public domain. Well known examples include the MNIST dataset for classification and the OxfordIIITPET dataset for segmentation. This article explains how TensorFlow Datasets work...
How to Architect a TinyML Application with an RTOS
An interesting question I’ve been asked on several occasions is, “How do I use machine learning with an RTOS?”. As machine learning finds its way into more applications, there will be applications that target low-power, clock-limited, edge...
Is Machine Learning Ready for Microcontroller-based Systems?
“Machine learning” is currently technologies number one hype word. Mention machine learning and venture capitalists open their checkbooks with visions of riches and grandeur. Developers froth at the mouth with opportunities and dreams of...
