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. Let’s get started. Update Oct/2019: Removed discussion of parametric/nonparametric models (thanks Alex). Overview […]
Supervised and Unsupervised Machine Learning Algorithms
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 […]
Parametric and Nonparametric Machine Learning Algorithms
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. Let’s get started. Learning a Function Machine learning can be summarized as learning a function (f) that maps input variables (X) to output […]
How Machine Learning Algorithms Work (they learn a mapping of input to output)
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. Le’s get started. Let’s get started. Learning a Function Machine learning algorithms are […]
Machine Learning Terminology from Statistics and Computer Science
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 […]
R Machine Learning Mini-Course
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. Let’s get started. Who Is This […]
Do Not Use Random Guessing As Your Baseline Classifier
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). Let’s […]
How To Get Started With Machine Learning in R (get results in one weekend)
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 […]
Machine Learning Evaluation Metrics in R
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. Let’s get started. Model Evaluation Metrics in R There are many different metrics that you can use to evaluate your machine […]
Compare The Performance of Machine Learning Algorithms in R
How do you compare the estimated accuracy of different machine learning algorithms effectively? In this post you will discover 8 techniques that you can use to compare machine learning algorithms in R. You can use these techniques to choose the most accurate model, and be able to comment on the statistical significance and the absolute […]