The Data Science Lab


Kernel Ridge Regression Using C#

KRR is especially useful when there is limited training data, says Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step tutorial.

Linear Ridge Regression Using C#

Implementing LRR from scratch is harder than using a library like scikit-learn, but it helps you customize your code, makes it easier to integrate with other systems, and gives you a complete understanding of how LRR works.

Gaussian Process Regression Using the scikit Library

Dr. James McCaffrey of Microsoft Research offers a full-code, step-by-step tutorial for this technique, especially useful when there is limited training data.

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Regression Using scikit Kernel Ridge Regression

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on this regression technique, which is especially useful when there is limited training data.

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Binary Classification Using a scikit Neural Network

Machine learning with neural networks is sometimes said to be part art and part science. Dr. James McCaffrey of Microsoft Research teaches both with a full-code, step-by-step tutorial.

Gaussian Naive Bayes Classification Using the scikit Library

Dr. James McCaffrey of Microsoft Research says the main advantage of using Gaussian naive Bayes classification compared to other techniques like decision trees or neural networks is that you don't have to fine tune model parameters.

Classification Using the scikit k-Nearest Neighbors Module

Dr. James McCaffrey of Microsoft Research uses a full-code, step-by-step demo to predict the species of a wheat seed based on seven predictor variables such as seed length, width and perimeter.

Regression Using a scikit MLPRegressor Neural Network

Dr. James McCaffrey of Microsoft Research uses a full-code, step-by-step demo to show how to predict the annual income of a person based on their sex, age, state where they live and political leaning.

Multinomial Naive Bayes Classification Using the scikit Library

A full-code demo from Dr. James McCaffrey of Microsoft Research shows how to predict the type of a college course by analyzing grade counts for each type of course.

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Multi-Class Classification Using a scikit Neural Network

Dr. James McCaffrey of Microsoft Research says a neural network model is arguably the most powerful multi-class classification technique.

Multi-Class Classification Using a scikit Decision Tree

Decision trees are useful for relatively small datasets that have a relatively simple underlying structure, and when the trained model must be easily interpretable, explains Dr. James McCaffrey of Microsoft Research, who provides step-by-step instructions and full source code.

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Naive Bayes Classification Using the scikit Library

Dr. James McCaffrey of Microsoft Research shows how to predict a person's sex based on their job type, eye color and country of residence.

Binary Classification Using a scikit Decision Tree

Dr. James McCaffrey of Microsoft Research says decision trees are useful for relatively small datasets and when the trained model must be easily interpretable, but often don't work well with large data sets and can be susceptible to model overfitting.

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Logistic Regression Using the scikit Library

Dr. James McCaffrey of Microsoft Research says the main advantage of scikit is that it's easy to use (even though most classes have many constructor parameters).

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Logistic Regression from Scratch Using Raw Python

The fundamental technique has been studied for decades, thus creating a huge amount of information and alternate variations that make it hard to tell what is key vs. non-essential information.

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