Overfitting is an undesirable machine learning behavior that occurs when the machine learning model gives

accurate predictions for training data but not for new data. When data scientists use machine learning

models for making predictions, they first train the model on a known data set. Then, based on this

information, the model tries to predict outcomes for new data sets. An overfit model can give inaccurate

predictions and cannot perform well for all types of new data. Suppose your model is overfitting. Which of

the following is NOT a valid way to try and reduce the overfitting?

a$)$ Reduce the noise in the training data.

b$)$ Decrease the model complexity.

c$)$ Improve the optimization algorithm being used for error minimization.

d$)$ Increase the amount of training data.

e$)$ all$-$of$-$them

f$)$ none$-$of$-$them