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Imbalanced-learn: Handling imbalanced class problem


In the previous article here, we have gone through the different methods to deal with imbalanced data. In this article, let us try to understand how to use imbalanced-learn library to deal with imbalanced class problems. We will make use of Pycaret library and UCI's default of credit card client dataset which is also in-built into PyCaret. Imbalanced-learn is a python package that provides a number of re-sampling techniques to deal with class imbalance problems commonly encountered in classification tasks. Note that imbalanced-learn is compatible with scikit-learn and is also part of scikit-learn-contrib projects.

Imbalanced Data -- Oversampling Using Gaussian Mixture Models


Gaussian mixture models (GMM) assume that there are a number of normally distributed subpopulations that the data comes from. Example: Suppose we have data on the height of 10,000 individuals. Below is a sample distribution. However, it actually comes from 4 different groups of people with different average heights that are normally distributed. One group has an average height of 150 cm, the others have average heights of 160, 170, and 180 cm.

8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset - Machine Learning Mastery


You are working on your dataset. You create a classification model and get 90% accuracy immediately. You dive a little deeper and discover that 90% of the data belongs to one class. 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 results on machine learning datasets with imbalanced data.

A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios Artificial Intelligence

Imbalance in the proportion of training samples belonging to different classes often poses performance degradation of conventional classifiers. This is primarily due to the tendency of the classifier to be biased towards the majority classes in the imbalanced dataset. In this paper, we propose a novel three step technique to address imbalanced data. As a first step we significantly oversample the minority class distribution by employing the traditional Synthetic Minority OverSampling Technique (SMOTE) algorithm using the neighborhood of the minority class samples and in the next step we partition the generated samples using a Gaussian-Mixture Model based clustering algorithm. In the final step synthetic data samples are chosen based on the weight associated with the cluster, the weight itself being determined by the distribution of the majority class samples. Extensive experiments on several standard datasets from diverse domains shows the usefulness of the proposed technique in comparison with the original SMOTE and its state-of-the-art variants algorithms.

Machine Learning with Imbalanced Data


Machine Learning with Imbalanced Data - Learn multiple techniques to tackle data imbalance and improve the performance of your machine learning models. Preview this Course - GET COUPON CODE Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models. If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets.