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Author Archives: jamesdmccaffrey
Gaussian Process Classification on the Wheat Seeds Dataset Using the scikit Library
A classification problem is one where the goal is to predict a single categorical value. For example, you might want to predict sex of a person (0 = male, 1 = female) based on age, income, and so on (a … Continue reading
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Parameterizing PyTorch Neural Network Architecture and Training Values for Evolutionary Optimization
When creating a neural network prediction model, you have to set values for the architecture (number hidden layers, number hidden nodes in each layer, hidden activation, etc.) and training (optimizer, batch size, etc.) In some scenarios you can manually experiment … Continue reading
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Sorting a Python List of Objects for Evolutionary Algorithms
When I implement an Evolutionary Algorithm using the Python language, I’m faced with the question of how to store possible solutions (typically vectors of floating point values) and their associated errors (typically a single floating point value). There are many … Continue reading
Posted in Machine Learning
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“Researchers Successfully Predict NFL Professional Football Scores” on the Pure AI Web Site
I contributed to an article titled “Researchers Successfully Predict NFL Professional Football Scores” on the April 2023 edition of the Pure AI web site. See https://pureai.com/articles/2023/04/03/zoltar.aspx. Zoltar is my prediction system for American NFL football. For the 2022-23 NFL season, … Continue reading
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An Example of AdaBoost Classification Using the scikit Library
Basic decision trees have several weaknesses and so there are many enhanced tree models. These include, in order of increasing complexity, bootstrap aggregation (“bagging”), random forest, adaptive boosting (“AdaBoost”), and gradient boosting. There are many variations of each of the … Continue reading
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A Quick Look at the FLAML Library for Automatic Machine Learning
I was sitting at home one weekend, waiting for the rain to stop so I could walk my dogs. I have spoken at the PyData conference before, but I’m not speaking this year. I was scanning through the conference agenda … Continue reading
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Don’t Blindly Trust All Machine Learning Datasets
I ran across a machine learning example that used the California Housing dataset. I didn’t know much about that dataset so I did a little exploration. I loaded and examined the dataset using the scikit library: from sklearn.datasets import fetch_california_housing … Continue reading
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An Example of Random Forest Classification Using the scikit Library
Basic decision trees have several weaknesses and so there are many enhanced tree models. These include, in order of increasing complexity, bootstrap aggregation (“bagging”), random forest, adaptive boosting (“AdaBoost”), and gradient boosting. There are many variations of each of the … Continue reading
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“Regression Using a scikit MLPRegressor Neural Network” in Visual Studio Magazine
I wrote an article titled “Regression Using a scikit MLPRegressor Neural Network” in the May 2023 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2023/05/01/regression-scikit.aspx. A regression problem is one where the goal is to predict a single numeric value. For … Continue reading
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Sorting a Python List of Tuples for Evolutionary Algorithms
When I implement an Evolutionary Algorithm using the Python language, I’m faced with the question of how to store possible solutions (typically vectors) and their associated errors (typically a single float32 value). There are many, many, many design possibilities that … Continue reading
Posted in Machine Learning
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.NET Test Automation Recipes
Software Testing
SciPy Programming Succinctly
Keras Succinctly
R Programming
James D. McCaffrey 
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