April 2020
Beginner to intermediate
156 pages
4h 47m
English
Content preview from Hands-On One-shot Learning with Python
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Imbalanced learning
In the imbalanced learning scenario, we have an imbalanced dataset; that is, we have more samples from one class than we do from other categories. This is also popularly known as a skewed distribution dataset. Let's take a look at some popular methods for dealing with a skewed dataset:
- Choice of metric: There are various forms of metrics that can help in assessing the accuracy of a model, such as a confusion matrix, precision, recall, and F1-score.
- Choice of algorithm: Parametric algorithms learn their parameters through the dataset, so if the dataset is biased, it is most likely that the parametric model will also be biased. Non-parametric approaches (for example, k-Nearest Neighbor) and ensembles (for example, AdaBoost, ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.