Member-only storyCaroline ArnoldFollowTowards Data Science--ShareMachine Learning relies on statistics, and Random Numbers are important to the performance of many steps in the data processing and model training pipeline. Modern Machine Learning frameworks provide abstractions and functions that implement randomness under the hood, and for us as data scientists and machine learning engineers, the details of random number generation often remain obscure.In this article, I want to shed some light on random numbers in machine learning. You will read about:By the end of this article, you will know what happens when you use random numbers in your machine learning pipeline, and you will learn the necessary lines of code to ensure reproducibility of your machine learning algorithms.To illustrate the importance of random numbers, we discuss three examples where they are relevant along the machine learning pipeline.Train/test splitSplitting your dataset into training and test data is one of the most important steps in evaluating the performance of a machine learning algorithm. We are interested in creating models that generalize well to data not used during training. To this end, a collection of data samples is divided into at least two disjoint sets.The training data is used to train the algorithm, i.e. to iteratively fix the model parameters. The test data is used to validate the algorithm by applying a trained model to test data and reporting appropriate metrics.----Towards Data ScienceAI consultant for Earth, Climate, Environment. I write about data science, visualization, and machine learning. https://medium.com/friday-data-storyCaroline ArnoldinTowards Data Science--9Damian GilinTowards Data Science--24Adrian H. RaudaschlinTowards Data Science--19Caroline ArnoldinCantor’s Paradise--2Everton Gomede, PhD--Luís OliveirainLevel Up Coding--3Learn with Nas--2Piero PaialungainTowards Data Science--2Deepak Chopra | Talking Data ScienceinTowards AI--23Mark A. Herschberg--16HelpStatusAboutCareersBlogPrivacyTermsText to speechTeams
