Neural Network Tutorials - Herong's Tutorial Examples

∟Performance Evaluation Metrics

∟MSE (Mean Squared Error)

This section describes MSE (Mean Squared Error) as a metric to evaluate the performance of a continuous prediction model.

What Is MSE (Mean Squared Error)? - MSE is a commonly used metric to evaluate the performance of a continuous prediction model. It takes the average value of squared errors of all samples.

Given a prediction model and a set of test samples, the MSE of the model on the test set is defined below:

where:

Obviously, if MSE = 0, the model is 100% accurate on the test set.

Table of Contents

 About This Book

 Deep Playground for Classical Neural Networks

 Building Neural Networks with Python

 Simple Example of Neural Networks

 TensorFlow - Machine Learning Platform

 PyTorch - Machine Learning Platform

 Gradio - ML Demo Platform

 CNN (Convolutional Neural Network)

 RNN (Recurrent Neural Network)

 GNN (Graph Neural Network)

 GAN (Generative Adversarial Network)

►Performance Evaluation Metrics

►MSE (Mean Squared Error)

 CI (Concordance Index)

 PCC (Pearson Correlation Coefficient)

 References

 Full Version in PDF/EPUB