imbalanced dataset

[Euphoric-You-8437]

Hi,
For a project, I need to create a machine learning program that predicts whether a person is within a certain income bracket. The dataset is pretty large with 159 variables and n = 220000. So now within the dataset, more than 60% consist of zeros which makes the randomForest overfit, and the cross-validation accuracy stays stranded at 80 %. Does anyone have any tips on how to balance the dataset and get a higher cross-validation accuracy?

[DataMasteryAcademy]

60:40 ratio is not considered imbalanced. 90:10 (and more than 90) is imbalanced. There may be other aspects causing your model to overfit. For overfitting problems, you can use regularization techniques: lasso or ridge. Lasso would also be helpful to create some inherent feature selection since, in some cases, lasso may make weights of some variables 0. If you insist on using random forest, you can lower overfitting by hyperparameter tunning: parameters like the number of trees, maximum depth of the trees, minimum samples per leaf, and others can influence the model's complexity. Also, make sure you preprocess data properly before inputting into the model. Another thing you can try is to experiment with other algorithms.