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Author Archives: jamesdmccaffrey
Naive Bayes Classification Example Using the scikit Library
Naive Bayes classification is a classical machine learning technique. It is best used when the predictor variables are all nonnumeric. Naive Bayes works for both binary classification and multiclass classification. And naive Bayes works well when you don’t have very … Continue reading
Posted in Scikit
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Understanding the PyTorch Linear Layer Default Weight and Bias Initialization
When a PyTorch neural network is instantiated, it’s common practice to use implicit weight and bias initialization. In the case of a Linear layer, the PyTorch documentation is not clear, and the source code is surprisingly complicated. I spent several … Continue reading
A Simplified Version of the scikit Library make_circles() Function
I was looking at spectral clustering with the scikitlearn library. Standard kmeans clustering doesn’t work well for data that has weird geometry. A standard example is data that when graphed looks like two concentric circles. Spectral clustering connects data into … Continue reading
Posted in Machine Learning
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“Researchers Evaluate the Top Four AI Stories of 2022” on the Pure AI Web Site
I contributed to an article titled “Researchers Evaluate the Top Four AI Stories of 2022” in the January 2023 edition of the Pure AI web site. See https://pureai.com/articles/2023/01/05/topaistoriesof2022.aspx. I am a regular contributing editor for the Pure AI site. For … Continue reading
Posted in Machine Learning
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What Are Correct Values for Precision and Recall When the Denominators Are Zero?
I did an Internet search for “What are correct values for precision and recall when the denominators equal 0?” and was pointed to a StackExchange page which had been up for over 11 years — and which was somewhat ambiguous. … Continue reading
Posted in Machine Learning
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NFL 2022 Season Super Bowl LVII Prediction – Zoltar Predicts the Chiefs Will Beat the Eagles
Zoltar is my NFL football prediction computer program. It uses reinforcement learning and a neural network. Here are Zoltar’s predictions for week #22 (Super Bowl LVII) of the 2022 season. Zoltar: chiefs by 3 dog = eagles Vegas: eagles by … Continue reading
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“Logistic Regression from Scratch Using Raw Python” in Visual Studio Magazine
I wrote an article titled “Logistic Regression from Scratch Using Raw Python” in the January 2023 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2023/01/18/logisticregression.aspx. Logistic regression is a machine learning technique for binary classification. For example, you might want to … Continue reading
Posted in Machine Learning
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Solving the Traveling Salesman Problem (TSP) Using an EpsilonGreedy Algorithm
An epsilongreedy algorithm is a general approach that can be used for many different problems. I recently devised a nice evolutionary algorithm for the Traveling Salesman Problem (TSP) that seems to work very well. Just for fun, I spent one … Continue reading
Posted in Machine Learning
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Another Look at GPT3 / Codex / GitHub Copilot – I Have Mixed Opinions
GPT3 (“Generative Pretrained Transformer”) is a large language model that can generate text, such as a response to, “Write two paragraphs about the history of computer programming.” GPT3 was trained on an enormous corpus of text — Wikipedia, books, blogs, … Continue reading
Posted in Machine Learning
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Binary Classification Using a scikit Decision Tree
I hadn’t looked at using a decision tree from the scikitlearn (scikit for short) library for several months, so I figured to do an example. Before I go any further: I am not a big fan of decision trees and … Continue reading
Posted in Scikit
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