Can the training error be 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).

What is a good MSE for neural network?

I know that an ideal MSE is 0, and Coefficient correlation is 1. Now for my case i get the best model that have MSE of 0.0241 and coefficient of correlation of 93\% during training. During validation the model provided MSE of 0.0585 and R2 of 85\%.

What is sum of squared error in neural network training?

The relevance of using sum-of-squares for neural networks (and many other situations) is that the error function is differentiable and since the errors are squared, it can be used to reduce or minimize the magnitudes of both positive and negative errors.

Why is the error of the training data zero?

Since your test sample is in the training dataset, it’ll choose itself as the closest and never make mistake. For this reason, the training error will be zero when K = 1, irrespective of the dataset.

What does training error mean?

Training error is the prediction error we get applying the model to the same data from which we trained. Training error is much easier to compute than test error. Train error is often lower than test error as the model has already seen the training set.

Is lower mean squared error better?

There is no correct value for MSE. Simply put, the lower the value the better and 0 means the model is perfect.

Why not mean square error may not be useful to train a neuron with sigmoid activation function?

On a final note, MSE is a good choice for a Cost function when we are doing Linear Regression (i.e fitting a line through data for extrapolation). In the absence of any knowledge of how the data is distributed assuming normal/gaussian distribution is perfectly reasonable.

How is MSE calculated in neural network?

The error is calculated by subtracting the output A from target T . Then the mean squared error is calculated. Note that mse can be called with only one argument because the other arguments are ignored. mse supports those ignored arguments to conform to the standard performance function argument list.

Why do we normalize the data prior to training a network?

Among the best practices for training a Neural Network is to normalize your data to obtain a mean close to 0. Normalizing the data generally speeds up learning and leads to faster convergence.