SMOTE Synthetic Minority Oversampling Technique for Handling Imbalanced Datasets

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=U3X98xZ4_no

Whenever we do classification in ML, we often assume that target label is evenly distributed in our dataset. This helps the training algorithm to learn the features as we have enough examples for all the different cases. For example, in learning a spam filter, we should have good amount of data which corresponds to emails which are spam and non spam. • SMOTE synthesises new minority instances between existing (real) minority instances. • If you do have any questions with what we covered in this video then feel free to ask in the comment section below I'll do my best to answer those. • If you enjoy these tutorials would like to support them then the easiest way is to simply like the video give it a thumbs up also it's a huge help to share these videos with anyone who you think would find them useful. • Please consider clicking the SUBSCRIBE button to be notified for future videos thank you all for watching. • You can find me on: • GitHub - https://github.com/bhattbhavesh91 • Medium - / bhattbhavesh91 • #ClassImbalance #SMOTE #SyntheticMinorityOversamplingTechnique #machinelearning #python #deeplearning #datascience #youtube

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