Why is training accuracy not 100\%?

A statistical model that is complex enough (that has enough capacity) can perfectly fit to any learning dataset and obtain 100\% accuracy on it. But by fitting perfectly to the training set, it will have poor performance on new data that are not seen during training (overfitting). Hence, it’s not what interests you.

What does it mean if test accuracy is higher than train accuracy?

How to interpret a test accuracy higher than training set accuracy. Most likely culprit is your train/test split percentage. Imagine if you’re using 99\% of the data to train, and 1\% for test, then obviously testing set accuracy will be better than the testing set, 99 times out of 100.

What if training accuracy is low and testing accuracy is high?

1 Answer. By definition, when training accuracy (or whatever metric you are using) is higher than your testing you have an overfit model.

Why is overfitting a problem?

Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.

Is an accuracy of 99.9\% always good for a predictive model?

Bayes’ theorem tells us that to be useful, our predictive algorithm needs to be more than 99.9\% accurate.

Can a model have 100\% accuracy?

OVERFITTING. Yes, a predictive model with 100\% accuracy is possible.

When the performance on the validation set is getting worse training on the model is stopped immediately?

Early stopping is a kind of cross-validation strategy where we keep one part of the training set as the validation set. When we see that the performance on the validation set is getting worse, we immediately stop the training on the model.

What is the problem if a predictive model is trained and evaluated on the same dataset?

Tackling Overfitting A split of data 66\%/34\% for training to test datasets is a good start. Using cross validation is better, and using multiple runs of cross validation is better again. You want to spend the time and get the best estimate of the models accurate on unseen data.