Category Archives: Machine Learning

RBF networks for regression had a brief period of popularity in the 1990s. They are now rarely used because other techniques, notably, neural networks, kernel ridge regression, and gradient boost regression, are much easier to train and typically have better … Continue reading →

One Saturday morning, after I walked my two dogs (Riley and Kevin) and a visiting dog (Llama), I figured I would take a look at the XBGoost (“extreme gradient boosting”) regression code library. The goal of a machine learning regression … Continue reading →

Arguably the most basic form of machine learning regression is linear regression. The liner regression prediction model is y’ = (x0 * w0) + (x1 * w1) + . . (xn * wn) + b. The y’ is the predicted … Continue reading →

The goal of a machine learning regression problem is to predict a single numeric value. For example, a bank might to predict the maximum safe loan amount to a customer based on customer age, current savings balance, and so on. … Continue reading →

Technically, a deep neural network is one that has two or more hidden layers of processing nodes. But deep neural networks for image processing or natural language processing typically have dozens or hundreds of hidden layers. A special case is … Continue reading →

I fed my decision tree regression system, implemented using from-scratch Python with NumPy, to several AI systems and asked the AI to analyze it for correctness. I was quite impressed that the AI found a few rare edge cases where … Continue reading →

The goal of a machine learning regression problem is to predict a single numeric value, for example, predicting the bank account balance of a person based on his age, annual income, and so on. A prediction model can be evaluated … Continue reading →

I decided to implement Gradient Boost regression using Blind Trees learners. Bottom line: For my demo dataset, the technique worked about the same as the standard architecture that uses regular decision trees as the learners. In machine learning, explaining what … Continue reading →