Question

Which of the following classification techniques may suffer from the overfitting issue? Choose all that apply. A. Decision Tree B. Logistic Regression c. Neural Network d. Naive Bayes

Answer 1

Actually Overfitting is very common for any machine learning model...

overfitting in the sense the machine learning model have high variance and low bias then the model is called overfitting.

by develop the tree overfitting occurs.by removing over fitting in decision tree we used pre-pruning or post-pruning techniques

logistic regression also faced overfitting model..MNIST data have many sample when we train the data and later test the data we can observe the overfitting.it have high variance

neural network also have overfitting when the training set error is small.when new data present in the neural network by applying forward pass and back propagation error is too large this make overfit the neural network...

naive bayes also sometimes faced overfitting when train is not properly.naive bayes suffer with underfitting in the sense high bias..we used laplase smoothing to avoid this...

all are faced overfitting

#if you have any doubts comment below...