How can I evaluate my regression neural network model?

How can I evaluate my regression neural network model?

I am new to machine learning and created a neural network for regression output. I have ~95000 training examples and ~24000 test examples. I want to know how can I evaluate my model and get train and test errors? How to know the accuracy of this regression model?

How to interpret loss and accuracy for a neural network?

Loss value implies how well or poorly a certain model behaves after each iteration of optimization. Ideally, one would expect the reduction of loss after each, or several, iteration(s). The accuracy of a model is usually determined after the model parameters are learned and fixed and no learning is taking place.

Which is the best measure of accuracy for regression?

MAPE is a pretty good measure for time series data, but will not work if has zero in the data or in the regression. The most used RMSE and MSE are used to minimize the error, not necessarily the accuracy. The most close to zero better the measure.

How to develop deep learning models for multi-output regression?

Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems. Neural network models for multi-output regression tasks can be easily defined and evaluated using the Keras deep learning library. In this tutorial, you will discover how to develop deep learning models for multi-output regression.

How are neural networks different from linear regression?

If we want to schematise at extreme, we could say that neural networks are the very complex “evolution” of linear regression designed to be able to model complex structures in the data. Let us consider, for example, a regression or a classification problem.

How to do the overall F test for regression?

This test is known as the overall F-test for regression . F = MSM / MSE = (explained variance) / (unexplained variance) Find a (1 – α)100% confidence interval I for (DFM, DFE) degrees of freedom using an F-table or statistical software. Accept the null hypothesis if F ∈ I; reject it if F ∉ I.

What is the F value of a hamster regression?

The F-value is 5.991, so the p-value must be less than 0.005. Verify the value of the F-statistic for the Hamster Example. For simple linear regression, R 2 is the square of the sample correlation r xy .

How to increase the accuracy of neural networks?

If you run your learning algorithm for 1 million iterations you will normally get a better score than if you run it for just 10 iterations, but it can be due to memorization of your training set: you ANN learn only to predict the outcome of those training sets, but will do poorly trying to predict data it has not seen before.

How to increase accuracy of neural networks in keras?

For creating an image classifier in keras I would suggest trying a convolutional neural network as they tend to work much better for images. Also, normalizing between layers can help with accuracy during training which should help yield a better validation/test accuracy. (The same concept as normalizing data before training.)

How to identify if your neural network is overfitting?

How to identify if your model is overfitting? you can just cross check the training accuracy and testing accuracy. If training accuracy is much higher than testing accuracy then you can posit that your model has overfitted. You can also plot the predicted points on a graph to verify. There are some techniques to avoid overfitting:

How are neural networks used in data science?

Neural networks are machine learning algorithms that provide state of the accuracy on many use cases. But, a lot of times the accuracy of the network we are building might not be satisfactory or might not take us to the top positions on the leaderboard in data science competitions.