Machine Learning.

Decision Trees Are Usually Better Than Logistic Regression
25 October 2018 | by Tim Bock

Logistic regression is a standard approach to building a predictive model. However, decision trees are an alternative which are clearer and often superior.

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Feature Engineering for Categorical Variables
24 October 2018 | by Tim Bock

There are two types of predictors in predictive models: numeric and categorical. There are several methods of transforming categorical variables.

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Feature Engineering for Numeric Variables
24 October 2018 | by Tim Bock

When building a predictive model, it is often practical to improve predictive performance by modifying the numeric variables. This is called transformation.

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view of a forest
How Random Forests Fit to Data
06 August 2018 | by Jake Hoare

A random forest is a collection of decision trees, which is used to learn patterns in data and make predictions based on those patterns.

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tree on a hill
How is Splitting Decided for Decision Trees?
02 August 2018 | by Jake Hoare

Decision trees work by repeatedly splitting the data to lead to the option which causes the greatest improvement. We explain how these splits are chosen.

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How is Variable Importance Calculated for a Random Forest?
30 July 2018 | by Jake Hoare

A random forest is an ensemble of decision trees. Like other machine-learning techniques, random forests use training data to learn to make predictions.

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Machine learning pruning
Machine Learning: Pruning Decision Trees
04 July 2017 | by Jake Hoare

Machine learning is a problem of trade-offs. Here I look at pruning and early stopping for managing these trade-offs in the context of decision trees.

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