sir could you explain more how the labels evolves and what does mean by capturing strong label signals, does it mean labels which comes with a data which makes the prediction more accurate
Hi,
Could you specify which courses and weeks you are talking about?
course 2 >>week 1>> labelling data>> Video Process Feedback and Human Labeling
Machine Learning Data Lifecycle in Production
Hello
You can take Capturing strong label signals as improving the quality and reliability of the labels used for training the model. Strong label signals lead to better model performance and accuracy.
Was that your question?
yes it’s answer for my second half of the question but what about label evolving
You mean how labels evolve during the training process?
- Initial Labels: When you start training a machine learning model, you begin with a set of labeled data. These labels are either manually annotated by humans or derived from some other source. The quality and size of this labeled dataset play a crucial role in the model’s performance.
- Model Training: The initial labeled data is used to train the machine learning model. The model learns from this data and tries to find patterns and relationships between inputs and labels to make predictions.
- Prediction and Feedback: Once the model is trained, it can be used to make predictions on new, unseen data. These predictions are then compared to the actual ground truth labels to assess the model’s performance.
- Model Evaluation: During the model evaluation process, metrics such as accuracy, precision, recall, F1 score, etc., are calculated to measure the model’s performance. These metrics indicate how well the model is generalizing to new data and making accurate predictions.
Also note about “Capturing strong label signals” you can achieve so by hiring domain experts or trained annotators
so when we are comparing the ground truth labels to the prediction , then label evolves?