MLwithGoExamples

This repository contains practical examples from the book 'Machine Learning with Go'. All examples are designed to provide hands on experience and understanding of the core machine learning concepts.

Chapter 3

The data file time_series.csv includes two columns, one for the predicted value and one for the observed value (floating point numbers). You can calculate three metrics from this data set:

• Mean-squared error
• Mean Absolute error
• R-squared

To compute, run the following:

 go run chapter3/mean_squared_error.go chapter3/time_series.csv

Alternatively, consider scenarios where observed and predicted data sets are categories. The categories in labeled.csv are 0, 1, and 2. You can see the count of true positives and false positives (where the observed category matches the predicted category) as follows:

  go run chapter3/category_accuracy.go chapter3/labeled.csv

Consider testing against the '0' category:

• If the predicted value and observed value are both 0, this is a true positive (TP)
• If the predicted value is 0 but the observed value isn't, this is a false positive (FP)
• If the predicted value isn't 0 but the observed is, this is a false negative (FN)
• If neither the predicted value nor the observed value is 0, this is a true negative (TN)

You can thus create metrics:

• Accuracy: The ratio of true predictions vs false predictions: (TP+TN)/(FP+FN+TP+TN)
• Precision: The ratio of true predictions over all predictions: TP/(TP+FP)
• Recall

To evaluate data, consider creating training and testing sets. You can subsample one data set into two distinct sets using the following command:

  go run chapter3/subsample.go chapter3/time_series.csv