Book Image

Julia Cookbook

By : Raj R Jalem, Jalem Raj Rohit
Book Image

Julia Cookbook

By: Raj R Jalem, Jalem Raj Rohit

Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Table of Contents (12 chapters)

Performance evaluation and model selection

Analysis of performance is very important for any analytics and machine learning processes. It also helps in model selection. There are several evaluation metrics that can be leveraged on ML models. The technique depends on the type of data problem being handled, the algorithms used in the process, and also the way the analyst wants to gauge the success of the predictions or the results of the analytics process.

Getting ready

To get ready, the MLBase library has to be installed and imported. So, as we already installed it for the Preprocessing recipe, we don't need to install it again. Instead, we can directly import it using the using MLBase command.

How to do it...

  1. Firstly, the predictions and the ground truths need to be defined in order to evaluate the accuracy and performance of a machine learning model or an algorithm. They can take a simple form of a Julia array. This is how they can be defined:

    truths = [1, 2, 2, 4, 4, 3, 3, 3, 1]
    pred   = ...