Archives for Imbalance data

21 May

Why the high accuracy in classification is not always correct?

Sourabh Mehta accuracy classification problem

The high accuracy of classification model could be misleading.

The post Why the high accuracy in classification is not always correct? appeared first on Analytics India Magazine.

27 Jan

How can SMOTE technique improve the performance of weak learners?

Vijaysinh Lendave data science

In this article, we will discuss how SMOTE technique can be used to improve the performance of weak learners such as SVM.

03 Apr

Hands-on Guide to LoRAS: A Better Oversampling Algorithm

Aditya Singh class imbalance

Localized Randomized Affine Shadowsampling (LoRAS) locally approximates the manifold by generating a random convex combination of noisy minority class data points.

The post Hands-on Guide to LoRAS: A Better Oversampling Algorithm appeared first on Analytics India Magazine.

29 Oct

Using Near-Miss Algorithm For Imbalanced Datasets

Bhoomika Madhukar balanced data

In this article, we will learn about the near-miss algorithm, the different versions of it and implement the different versions on an imbalanced dataset.

The post Using Near-Miss Algorithm For Imbalanced Datasets appeared first on Analytics India Magazine.

15 Feb

5 Important Techniques To Process Imbalanced Data In Machine Learning

Ambika Choudhury Imbalance data

Imbalance data distribution is an important part of machine learning workflow. An imbalanced dataset means instances of one of the two classes is higher than the other, in another way, the number of observations is not the same for all the classes in a classification dataset. This problem is faced not only in the binary…

The post 5 Important Techniques To Process Imbalanced Data In Machine Learning appeared first on Analytics India Magazine.