An attractive way to display these decisions is to create a Pythagorean Tree, where the sides of each triangle relate to the number of observations split by each decision. ![]() Pedalling balance, fastest 500m and duration separated the remaining rides. After this, turbo trainer sessions seemed to have a high level of TISS Aerobicity, which relates to the percentage of effort done aerobically. Then Average Power Variance helped identify races, as observed in the previous blog. The first split separates the majority of training rides from races and turbo trainer sessions, based on an average speed of 35.8km/h. The algorithm finds an efficient way to make a series of binary splits of the data set, in order to arrive at a set of criteria that separates the classes, as illustrated below. Perhaps the most basic approach is to build a Decision Tree. ![]() Here we consider Decision Trees, Random Forests and Support Vector Machines. The Orange software, used previously, makes it extremely easy to compare a number of simple models that map a ride’s statistics to its type: race, turbo trainer or just a training ride. ![]() The K-means algorithm was an example of unsupervised learning that identified clusters of similar observations without using any identifying labels. In the previous blog, I explored the structure of a data set of summary statistics from over 800 rides recorded on my Garmin device.
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