1/5/2024 0 Comments Querious query alalysis![]() You might want to predict whether a patient will get pancreatic cancer, however, you might only have the opportunity to give a small number of patients further examinations to collect features, etc. ![]() Let's take the example of studying pancreatic cancer. ![]() In many settings, there can be limiting factors that hamper gathering large amounts of labelled data. One of the more time-consuming tasks in passive learning is collecting labelled data. You will call this typical method passive learning. These are tasks which involve gathering a large amount of data randomly sampled from the underlying distribution and using this large dataset to train a model that can perform some sort of prediction. The main hypothesis in active learning is that if a learning algorithm can choose the data it wants to learn from, it can perform better than traditional methods with substantially less data for training.īut what are these traditional methods exactly? Later, you will see some of the most popular methods for querying data points. How the active learning query method was able to select such good points is one of the major research areas within active learning. This improvement comes from selecting superior data points so that the classifier was able to create a very good decision boundary. This new decision boundary is significantly better as it better separates both colours. In the right-most picture, logistic regression is used again, but this time, you selected a small subset of points using an active learning query method. This skew is due to the poor selection of data points for labelling. This means that there will be many green data points that will be labelled incorrectly as red. This line is clearly skewed away from the red data points and into the green shapes area. However, you see that the decision boundary created using logistic regression (the blue line) is sub-optimal. In the middle picture, logistic regression is used to classify the shapes by first randomly sampling a small subset of points and labelling them. As a result, you would want to sample a small subset of points and find those labels and use these labelled data points as your training data for a classifier. However, you can assume that you do not know the labels (red or green) of the data points, but trying to find the label for each of them would be very expensive. Astute readers will know that this is a classification task and you would like to create a 'decision boundary' (in this case, it's just a line) that would separate the green and red shapes. Looking at the leftmost picture above (taken from this survey), you have two clusters, those coloured green and those coloured red. Rather than first giving a formal definition for active learning, I think it is better start with a simple example to give you a better understanding of why active learning works. In a next post, you will learn more about how you can use active learning in conjunction with transfer learning to optimally leverage existing (and new) data. Active learning can be thought of as a type of 'design methodology' similar to transfer learning, which can also be used to leverage small amounts of labelled data. Today's blog post will explain the reasoning behind active learning, its benefits and how it fits into modern day machine learning research.īeing able to properly utilise active learning will give you a very powerful tool which can be used when there is a shortage of labelled data. But you are not especially good with details you need others to help you deal with the smaller parts of the picture.Active learning is one of those topics you hear in passing but somehow never really got the time to fully understand. In general, you also see the methods necessary to fulfill that promise. You dream of big projects, great undertakings, and rewards. “You want success in its fullest meaning - wealth, power, and material comforts. You are a realist and a visionary planner.” Inner analysis of Querious by heart number 8 You are highly competitive and will not rest until you are satisfied that you have bypassed the opposition. Whatever your enterprise, you strive to be the best and most successful in your field. It is both your challenge and your birthright to gain dominion over a small part of the earth. “You have the power and potential to achieve great things. Talent analysis of Querious by expression number 8 Querious name Numerology Numerology (Expression Number)
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