how to prevent overfitting
ways to prevent overfitting include cross validation in which the data being used for training the model is chopped into folds or partitions and the model is run for each fold then the overall error estimate is averaged other methods include ensembling predictions are combined from at least two separate models data augmentation in which the available data set is made to look diverse and data simplification in which the model is streamlined to avoid overfitting financial professionals must always be aware of the dangers of overfitting or underfitting a model based on limited data the ideal model should be balanced overfitting in machine learningoverfitting is also a factor in machine learning it might emerge when a machine has been taught to scan for specific data one way but when the same process is applied to a new set of data the results are incorrect this is because of errors in the model that was built as it likely shows low bias and high variance the model may have had redundant or overlapping features resulting in it becoming needlessly complicated and therefore ineffective overfitting vs underfittinga model that is overfitted may be too complicated making it ineffective but a model can also be underfitted meaning it is too simple with too few features and too little data to build an effective model an overfit model has low bias and high variance while an underfit model is the opposite it has high bias and low variance adding more features to a too simple model can help limit bias overfitting examplefor example a university that is seeing a college dropout rate that is higher than what it would like decides it wants to create a model to predict the likelihood that an applicant will make it all the way through to graduation to do this the university trains a model from a dataset of 5 000 applicants and their outcomes it then runs the model on the original dataset the group of 5 000 applicants and the model predicts the outcome with 98 accuracy but to test its accuracy they also run the model on a second dataset 5 000 more applicants however this time the model is only 50 accurate as the model was too closely fit to a narrow data subset in this case the first 5 000 applications 