These large masses of data need to be labeled to make This is about them usable. Data that is structured and labeled properly can then be used to train and deploy models.
In the first episode of our Guided Labeling series ? An Introduction to Active Learning ? we looked at the human-in-the-loop cycle of active learning. In that cycle ? the system starts by picking examples it deems most valuable for learning ? and the This is about human labels them. Based on these initially labeled pieces of data ? a first model is trained. With this trained model ? we score all the rows for which we still have missing labels and then start active learning sampling. or re-ranking what the human-in-the-loop should be labeling next to best improve the model.
There are different active learning sampling strategies ? and in today’s blog post ? we want to look at the label density technique.
Label Density
When labeling data points ? the user might telegram data wonder about any of these questions:
“Is this row of my dataset representative of the distribution?”
“How many other still unlabeled data social media marketing for real estate agents points are similar to this one that I’ve already labeled?”
“Is this row unique in the dataset — is it an outlier?”
The above are all fair questions
For example ? if you only label outliers ? then your labeled as uae phone number Zoom ? training set won’t be as representative as if you had labeled the most common cases. On the other hand ? if you label only common cases of your dataset ? then your model would perform badly whenever it sees something just a bit exceptional to what you have labeled.