Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses the kernel matrix inverse (Cholesky decomposition) technique for model training. There is no single best machine learning regression technique, but when kernel ridge regression prediction works, it is often highly accurate.
- By James McCaffrey
- 09/15/2025
Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two possible training techniques. There is no single best machine learning regression technique, but when kernel ridge regression prediction works, it is often very accurate.
- By James McCaffrey
- 09/02/2025
Dr. James McCaffrey presents a complete end-to-end demonstration of computing the determinant of a matrix using the C# language. In machine learning scenarios, computing the determinant of a matrix is typically used during model training to determine if a matrix has an inverse or not.
- By James McCaffrey
- 08/19/2025
Dr. James McCaffrey presents a complete end-to-end demonstration of k-nearest neighbors regression using JavaScript. There are many machine learning regression techniques, but k-nearest neighbors is especially simple to implement and the results are highly interpretable.
- By James McCaffrey
- 08/04/2025
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Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two possible training techniques. There is no single best machine learning regression technique. When kernel ridge regression prediction works, it is often highly accurate.
- By James McCaffrey
- 07/14/2025
Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, and a good way to establish baseline results for comparison with other more sophisticated regression techniques.
- By James McCaffrey
- 07/07/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Cayley-Hamilton technique. Compared to other matrix inverse algorithms, Cayley-Hamilton is very simple and as a nice side effect gives you the matrix determinant. However, Cayley-Hamilton is not suitable for use with large matrices.
- By James McCaffrey
- 06/16/2025
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Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric value. A linear SVR model uses an unusual error/loss function and cannot be trained using standard techniques, and so particle swarm optimization training is used.
- By James McCaffrey
- 06/03/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton iteration is simple and easy to customize, but the technique is relatively slow.
- By James McCaffrey
- 05/15/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Compared to standard linear regression, which predicts a single numeric value based only on a linear combination of predictor values, linear regression with interactions can handle more complex data while retaining a high level of model interpretability.
- By James McCaffrey
- 05/02/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of Nadaraya-Watson kernel regression using the C# language. NW kernel regression is simple to implement and is especially effective for small datasets.
- By James McCaffrey
- 04/18/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric value. A linear SVR model uses an unusual error/loss function and cannot be trained using standard simple techniques, and so evolutionary optimization training is used.
- By James McCaffrey
- 04/01/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric value with an assurance such as, "The predicted y value is 0.6789 and there's roughly a 90% chance the prediction will be greater than or equal to the true y value."
- By James McCaffrey
- 03/17/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demo of Poisson regression, where the goal is to predict a count of things arriving, such as the number of telephone calls received in a 10-minute interval at a call center. When your source data is close to mathematically Poisson distributed, Poisson regression is simple and effective.
- By James McCaffrey
- 03/03/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the naive Bayes regression technique, where the goal is to predict a single numeric value. Compared to other machine learning regression techniques, naive Bayes regression is usually less accurate, but is simple, easy to implement and customize, works on both large and small datasets, is highly interpretable, and doesn't require tuning any hyperparameters.
- By James McCaffrey
- 02/20/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random neighborhoods regression technique, where the goal is to predict a single numeric value. Compared to other ML regression techniques, advantages are that it can handle both large and small datasets, and the results are highly interpretable.
- By James McCaffrey
- 02/03/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the gradient boosting regression technique, where the goal is to predict a single numeric value. Compared to existing library implementations of gradient boosting regression, a from-scratch implementation allows much easier customization and integration with other .NET systems.
- By James McCaffrey
- 01/15/2025
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to predict a single numeric value. The demo program uses C#, but it can be easily refactored to other C-family languages.
- By James McCaffrey
- 01/02/2025