Various types of sampling techniques for imbalanced datasets are discussed in depth with examples and analysis. Get yourself familiar with over-/under-sampling, SMOTE, ADA-SYN, sampling with cleaning, boosting, clustering, and more. Continue reading Imbalanced Learning: sampling techniques
In statistics and data mining, we often encounter the word ‘control’, mostly from terms like control variables and control groups. In fact, a control variable has slightly different meanings in different fields Continue reading Control Variable explained
In a machine learning project, after crawling or collecting data, we have to split it into at least 2 parts: training and validation data. Continue reading Splitting data into a Training set and a Validation set