Is it possible to reduce the training error to zero?

Is it possible to reduce the training error to zero?

You can get zero training error by chance, with any model. Say your biased classifier always predicts zero, but your dataset happens to be all labeled zero. Zero training error is impossible in general, because of Bayes error (think: two points in your training data are identical except for the label).

Does more training data increase bias?

It is clear that more training data will help lower the variance of a high variance model since there will be less overfitting if the learning algorithm is exposed to more data samples.

Why do we need training accuracy?

We need it to monitor how the learning process is going on. Is it learning something, is the learning rate good, etc. But whether if it is learning something useful (generalization) or useless (memorization/overfitting) can be inferred based on validation accuracy (or any other performance metric).

How many datasets hold out method keeps for training?

In hold-out method for model selection, the dataset is split into three different sets – training, validation and test dataset. The following process represents hold-out method for model selection: Split the dataset in three parts – Training dataset, validation dataset and test dataset.

Can testing accuracy be more than training accuracy?

2 Answers. Test accuracy should not be higher than train since the model is optimized for the latter. Even so there would need to be some element of “test data distribution is not the same as that of train” for the observed behavior to occur.

Which is better, training accuracy or test accuracy?

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. The solution here is to use 50% of the data to train on, and 50% to evaluate the model. Accuracy on the training set might be noise, depending on which ML algorithm you are using.

Why is training set should always be smaller than test set?

With larger datasets, any observable estimate in a sample becomes very close to its value on the population it has been drawn from. Larger test datasets ensure a more accurate calculation of model performance. Training on smaller datasets can be done by sampling techniques such as stratified sampling.

How does batch size affect training and accuracy?

As we will see, both the training and testing accuracy will depend on batch size so it’s more meaningful to talk about test accuracy rather than generalization gap. More specifically, we want the test accuracy after some large number of epochs of training or “asymptotic test accuracy” to be high.

Why is ML accuracy higher than training set?

Accuracy on the training set might be noise, depending on which ML algorithm you are using. The training set accuracy doesn’t evaluate the correctness of your model on unseen rows. One strategy is to ignore the training set accuracy.