  Machine learning models (mainly neural networks) are used more and more in
real life. Users feed their data to the model for training. But these processes
are often one-way. Once trained, the model remembers the data. Even when data
is removed from the dataset, the effects of these data persist in the model.
With more and more laws and regulations around the world protecting data
privacy, it becomes even more important to make models forget this data
completely through machine unlearning.
  This paper adopts the projection residual method based on Newton iteration
method. The main purpose is to implement machine unlearning tasks in the
context of linear regression models and neural network models. This method
mainly uses the iterative weighting method to completely forget the data and
its corresponding influence, and its computational cost is linear in the
feature dimension of the data. This method can improve the current machine
learning method. At the same time, it is independent of the size of the
training set. Results were evaluated by feature injection testing (FIT).
Experiments show that this method is more thorough in deleting data, which is
close to model retraining.
