This chapter discusses the recent rise of machine learning (ML) techniques, specifically decision trees and Random Forests (RFs). RFs do not pursue causal inference but are powerful techniques for making predictions. RFs are discussed from various angles and compared with regression techniques like Classification and Regression Trees (CART), Random Forests, Synthetic Minority Oversampling TEchniques (SMOTE), Confusion Matrix, and Receiver Operating Characteristic (ROC) curve. As a running example, the value of RFs is illustrated throughout the chapter by focusing on the context of the prediction of high-growth firms (HGFs, also known as “gazelles”) using Swedish data (limited liability firms, 2004–2015). RFs appear to outperform previously used regression techniques when the task is a prediction of HGFs. ML techniques such as RFs have “raised the bar” in terms of the accuracy of prediction tasks. Improved prediction accuracy can be expected to lead to better decision-making by various stakeholders, such as (actual and potential) entrepreneurs, investors, academics, and policy-makers. We contribute to the emergence of a new class of quantitative analysis tools for making predictions that are expected to have superior predictive power in many contexts, including the prediction of HGFs.