SMOTE (Synthetic Minority Oversampling Technique) for Handling Imbalanced Datasets
SMOTE stands for Synthetic Minority Oversampling Technique. This is a statistical technique for increasing the number of cases in your dataset in a balanced ...
Recent Posts
Class Weights for Handling Imbalanced Datasets
n scikit-learn, a lot of classifiers comes with a built-in method of handling imbalanced classes. If we have highly imbalanced classes and have no addressed ...
Undersampling for Handling Imbalanced Datasets
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 f...
ML Metrics: Sensitivity vs. Specificity
In this video we talk about Sensitivity and Specificity - two key concepts for evaluating Machine Learning methods. These make it easier to choose which meth...
Out-of-bag (OOB) score for Ensemble Classifiers in Sklearn
In the previous video we saw how OOB_Score keeps around 36% of training data for validation.This allows the RandomForestClassifier to be fit and validated wh...