How to use imbalance distribution in logistic regression?
In logistic regression, another technique comes handy to work with imbalance distribution. This is to use class-weights in accordance with the class distribution. Class-weights is the extent to which the algorithm is punished for any wrong prediction of that class.
When to use class weight for weighted logistic regression?
For majority class, will use weight of 1 and for minority class, will use weight of 99. So the penalty of wrong prediction of minority class would be 99 times more severe than wrong prediction of majority class. Moreover, with this class-weight values, we would expect our model to perform better then the default one i.e. previous model.
How is random resampling used for imbalanced classification?
There are two main approaches to random resampling for imbalanced classification; they are oversampling and undersampling. Random Oversampling: Randomly duplicate examples in the minority class. Random Undersampling: Randomly delete examples in the majority class.
How is random undersampling used in imbalanced learning?
Random undersampling involves randomly selecting examples from the majority class and deleting them from the training dataset. In the random under-sampling, the majority class instances are discarded at random until a more balanced distribution is reached. — Page 45, Imbalanced Learning: Foundations, Algorithms, and Applications, 2013
Why are minority classes imbalanced in logistic regression?
The problem is not that the classes are imbalanced per se, it is that there may not be sufficient patterns belonging to the minority class to adequately represent its distribution. This means that the problem can arise for any classifier (even if you have a synthetic problem and you know you have the true model), not just logistic regression.
How to use default weights in logistic regression?
After above test-train split, lets build a logistic regression with default weights. For minority class, above model is able to predict 14 correct out of 29 samples. For majority class, model got only one prediction wrong. Model is not doing a good job in predicting minority class.
What can you do with a logistic regression model?
Logistic regression gives us a mathematical model that we can we use to estimate the probability of someone volunteering given certain independent variables. The model that logistic regression gives us is usually presented in a table of results with lots of numbers.
How to use logistic regression in machine learning?
The scikit-learn Python machine learning library provides an implementation of logistic regression that supports class weighting. The LogisticRegression class provides the class_weight argument that can be specified as a model hyperparameter.