Dealing with Imbalanced Classes in Machine Learning
IntroductionMost real-world classification problems display some level of class imbalance, which is when each class does not make up an equal portion of your data-set. It is important to properly adjust your metrics and methods to adjust for your goals. If this is not done, you may end up optimizing for a meaningless metric in the context of your use case.
Imbalanced Data : How to handle Imbalanced Classification Problems
Introduction If you have spent some time in machine learning and data science, you would have definitely come across imbalanced class distribution. This is a scenario where the number of observations belonging to one class is significantly lower than those belonging to the other classes. This problem is predominant in scenarios where anomaly detection is crucial like electricity pilferage, fraudul
7 Techniques to Handle Imbalanced Data - KDNuggets
7 Techniques to Handle Imbalanced Data This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced. By Ye Wu & Rick Radewagen Introduction What have datasets in domains like, fraud detection in banking, real-time bidding in marketing or intrusion detection in networks, in common?
8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset - MachineLearningMastery.com
Has this happened to you? You are working on your dataset. You create a classification model and get 90% accuracy immediately. “Fantastic” you think. You dive a little deeper and discover that 90% of the data belongs to one class. Damn! This is an example of an imbalanced dataset and the frustrating results it can cause. In this post you will discover the tactics that you can use to deliver great
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