How to be smarter with your time-series labeling?
Most companies have more data than they know what to do with it. However, what really matters is data quality, and especially data label quality.
The performance of supervised models depends directly on the quality of the labels: poorly labelled data will almost always lead to poor results, despite how many more of the dataset you label.
So what is the solution?
The performance of supervised models depends directly on the quality of the labels: poorly labelled data will almost always lead to poor results, despite how many more of the dataset you label.
So what is the solution?
So what will you get?
The answer is to be clever about labelling. Labelling by hand each event one by one is simply not feasible. Thankfully, smarter ways of confidently labelling your entire dataset exist. In this article, we will look at different automation methods to speed up the labelling process.
With this whitepaper let's learn more about Reinforcement learning, Active learning, Pseudo labelling, Generative Adversarial Networks and Label propagation/spreading.
With this whitepaper let's learn more about Reinforcement learning, Active learning, Pseudo labelling, Generative Adversarial Networks and Label propagation/spreading.
