Machine Learning Evaluation Metrics in R

Last Updated on August 22, 2019 What metrics can you use to evaluate your machine learning algorithms? In this post you will discover how you can evaluate your machine learning algorithms in R using a number of standard evaluation metrics. Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples. Let’s get started. Machine Learning Evaluation Metrics in RPhoto by Roland Tanglao, some rights reserved. Model […]

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How To Get Started With Machine Learning in R (get results in one weekend)

Last Updated on December 13, 2019 How do you get started with machine learning in R? R is a large and complex platform. It is also the most popular platform for the best data scientists in the world. In this post you will discover the step-by-step process that you can use to get started using machine learning for predictive modeling on the R platform. The steps are practical and so simple that you could be able to build accurate predictive […]

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Do Not Use Random Guessing As Your Baseline Classifier

Last Updated on September 25, 2019 I recently received the following question via email: Hi Jason, quick question. A case of class imbalance: 90 cases of thumbs up 10 cases of thumbs down. How would we calculate random guessing accuracy in this case? We can answer this question using some basic probability (I opened excel and typed in some numbers). Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for […]

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R Machine Learning Mini-Course

Last Updated on August 22, 2019 From Developer to Machine Learning Practitioner in 14 Days In this mini-course you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using R in 14 days. This is a big and important post. You might want to bookmark it. Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples. Let’s […]

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Machine Learning Terminology from Statistics and Computer Science

Last Updated on August 8, 2019 Data plays a big part in machine learning. It is important to understand and use the right terminology when talking about data. In this post you will discover exactly how to describe and talk about data in machine learning. After reading this post you will know the terminology and nomenclature used in machine learning to describe data. Kick-start your project with my new book Statistics for Machine Learning, including step-by-step tutorials and the Python source […]

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How Machine Learning Algorithms Work (they learn a mapping of input to output)

Last Updated on August 12, 2019 How do machine learning algorithms work? There is a common principle that underlies all supervised machine learning algorithms for predictive modeling. In this post you will discover how machine learning algorithms actually work by understanding the common principle that underlies all algorithms. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples. Le’s get started. How Machine Learning Algorithms WorkPhoto by GotCredit, […]

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Parametric and Nonparametric Machine Learning Algorithms

Last Updated on August 15, 2020 What is a parametric machine learning algorithm and how is it different from a nonparametric machine learning algorithm? In this post you will discover the difference between parametric and nonparametric machine learning algorithms. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples. Let’s get started. Parametric and Nonparametric Machine Learning AlgorithmsPhoto by John M., some rights reserved. Learning a Function Machine learning […]

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Supervised and Unsupervised Machine Learning Algorithms

Last Updated on August 20, 2020 What is supervised machine learning and how does it relate to unsupervised machine learning? In this post you will discover supervised learning, unsupervised learning and semi-supervised learning. After reading this post you will know: About the classification and regression supervised learning problems. About the clustering and association unsupervised learning problems. Example algorithms used for supervised and unsupervised problems. A problem that sits in between supervised and unsupervised learning called semi-supervised learning. Kick-start your project with […]

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Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning

Last Updated on October 25, 2019 Supervised machine learning algorithms can best be understood through the lens of the bias-variance trade-off. In this post, you will discover the Bias-Variance Trade-Off and how to use it to better understand machine learning algorithms and get better performance on your data. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples. Let’s get started. Update Oct/2019: Removed discussion of parametric/nonparametric models […]

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Overfitting and Underfitting With Machine Learning Algorithms

Last Updated on August 12, 2019 The cause of poor performance in machine learning is either overfitting or underfitting the data. In this post, you will discover the concept of generalization in machine learning and the problems of overfitting and underfitting that go along with it. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples. Let’s get started. Overfitting and Underfitting With Machine Learning AlgorithmsPhoto by Ian Carroll, some […]

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