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The data scientist builds and trains prescriptive or descriptive models, then tests and evaluates the model to make sure it answers the question or addresses the business problem. At its simplest, a model is a piece of code that takes an input and produces output. Creating a machine learning model involves selecting an algorithm, providing it with data, and tuning hyperparameters. Hyperparameters are adjustable parameters that let data scientists control the model training process. For example, with neural networks, the data scientist decides the number of hidden layers and the number of nodes in each layer. Hyperparameter tuning, also called hyperparameter optimization, is the process of finding the configuration of hyperparameters that result in the best performance.

A common question is "Which machine learning algorithm should I use?" A machine learning algorithm turns a dataset into a model. The algorithm the data scientist selects depends primarily on two different aspects of the data science scenario:

To help answer these questions, Azure Machine Learning provides a comprehensive portfolio of algorithms, such as Multiclass Decision Forest, Recommendation systems, Neural Network Regression, Multiclass Neural Network, and K-Means Clustering. Each algorithm is designed to address a different type of machine learning problem. In addition, The Azure Machine Learning Algorithm Cheat Sheet helps data scientists choose the right algorithm to answer the business question.

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What is Data Science? How to be a Data Scientist ...