Flaky tests are test cases that can pass or fail without code changes. They often waste the time of software developers and obstruct the use of continuous integration. Previous work has presented several automated techniques for detecting flaky tests, though many involve repeated test executions and a lot of source code instrumentation and thus may be both intrusive and expensive. While this motivates researchers to evaluate machine learning models for detecting flaky tests, prior work on the features used to encode a test case is limited. Without further study of this topic, machine learning models cannot perform to their full potential in this domain. Previous studies also exclude a specific, yet prevalent and problematic, category of flaky tests: order-dependent (OD) flaky tests. This means that prior research only addresses part of the challenge of detecting flaky tests with machine learning. Closing this knowledge gap, this paper presents a new feature set for encoding tests, called Flake16. Using 54 distinct pipelines of data preprocessing, data balancing, and machine learning models for detecting both non-order-dependent (NOD) and OD flaky tests, this paper compares Flake16 to another well-established feature set. To assess the new feature set’s effectiveness, this paper’s experiments use the test suites of 26 Python projects, consisting of over 67,000 tests. Along with identifying the most impactful metrics for using machine learning to detect both types of flaky test, the empirical study shows how Flake16 is better than prior work, including (1) a 13% increase in overall F1 score when detecting NOD flaky tests and (2) a 17% increase in overall F1 score when detecting OD flaky tests.