This Machine Learning (ML) test assesses candidates’ technical knowledge of core machine learning concepts such as regularization, classification, and unsupervised learning. This machine learning test will help you to identify developers proficient in ML.
Classical regression methods and regularization
Classical classification methods and support vector machines
Tree-based and ensemble methods for regression and classification
Unsupervised learning, outlier detection, and dimensionality reduction
Machine learning engineers, data engineers, data analysts, data visualization creators, artificial intelligence engineers, entrepreneurs, decision makers, and any other roles requiring intermediate knowledge of Machine Learning.
In our age of information and technology, businesses across industries have to deal with massive amounts of data. It is crucial to leverage this data to obtain insights that can drive decision-making. Harnessing the power of machine learning can help your business to more easily adapt to ever-changing market conditions, improve business operations, and gain a deeper understanding of consumer needs.
This Machine Learning test evaluates candidates’ knowledge of the fundamental concepts in machine learning, including both classical and tree-based ensemble methods for regression and classification, supervised and unsupervised learning, outlier detection, and dimensionality reduction. The questions on this machine learning test focus on different scenarios when working with data, covering the different ML functionalities for dealing with each of them.
Candidates who perform well on the test have a strong knowledge of core machine learning approaches and can make the best use of these approaches when working with different types of information. They have all the necessary skills to help your business harness machine learning to power data-driven decision-making.
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Gary has been working in the data science field for more than three years and is proficient in the fields of machine learning and data analysis. He has a Bachelor’s degree in Economics and a Master’s degree in Computer Science. The combination of those two fields helps Gary to achieve even greater results.
He is fond of computer science and loves to work on projects related to Artificial Intelligence which is, in his opinion, the future of our world.
Reliability: Cronbach’s alpha coefficient = .63
Face validity: Candidates rated this test as accurately measuring their skills (average score of 3.66 out of 5.00).
Criterion-related validity: Candidates with higher scores on this test received higher average ratings from the hiring team during the selection process (r = .44, N = 352).
For an in-depth look at interpreting test results, please take a look at our Science series articles: How to interpret test fact sheets (part 1): Reliability, and How to interpret test fact sheets (part 2): Validity.
For an explanation of the various terms, please refer to our Science glossary.
Reliability and validity | Sufficient data available | Analyses and checks conducted | Outcome |
---|---|---|---|
Reliability | ✔ | ✔ | Acceptable |
Content validity | ✔ | ✔ | Acceptable |
Face validity | ✔ | ✔ | Acceptable |
Construct validity | ✔ | ✔ | Acceptable |
Criterion-related validity | ✔ | ✔ | Acceptable |
Group differences | |||
Age differences | Pending | Pending | Pending |
Gender differences | ✔ | ✔ | Acceptable |
Ethnicity differences | Pending | Pending | Pending |
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The Machine Learning test will be included in a PDF report along with the other tests from your assessment. You can easily download and share this report with colleagues and candidates.