Category Archives: Scikit

The goal of a regression problem is to predict a single numeric value. For example, you might want to predict the selling price of a house based on its square footage, number of bedrooms, age, and so on. The simplest … Continue reading →

I was presenting a talk recently. One of my examples used the scikit-learn code library for machine learning. One of the attendees asked about the difference between “scikit” and “scikit-learn”. Briefly, scikit-learn is a Python language code library for machine … Continue reading →

Briefly, I put together a demo of a scikit Gaussian process regression model that uses the polynomial kernel from the sklearn.metrics.pairwise module via the PairwiseKernel class. Whew, what a mouthful. A Gaussian process regressor model predicts a single numeric value … Continue reading →

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 →

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 →

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 →

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 →

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the median house price in a town near Boston based on the town’s crime rate, percentage of Black … Continue reading →

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 →

I wrote an article titled “Multinomial Naive Bayes Classification Using the scikit Library” in the April 2023 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2023/04/17/multinomial-naive-bayes.aspx. Naive Bayes classification is a classical machine learning technique to predict a discrete value. There … Continue reading →