machine learning
Model M
behaviour
ODEL STACKING is a machine-learning technique that combines the predictions of multiple models to make a better forecast. Imagine you have a group of bloodstock agents who are all trying to predict if a yearling is potentially an elite racehorse. Each agent will have their own unique perspective and will take their own view (let’s say a score from 1 to 100). If you took the average of the independent predictions of all the experts, you will get a better prediction than any individual expert could make. Model stacking works in a similar way. We train multiple models – some that are based on video recognition and some on image recognition – on the same dataset. Each model will learn different patterns in the data and will make its own prediction. Some models will be very precise (they have a high-strike rate for the ones they like, but miss a lot of good horses), while others will have higher recall (they like more of the good horses, but like a lot more in general), but having variation and ensuring that their predictions are different enough, but useful overall, is what builds a strong model stack. So, for instance, the diagram opposite shows the five models that we have as the
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www.internationalthoroughbred.net
WE HAVE DECIDED to extend this short run of features, taken as an edited version from Byron Rogers’s blog (www.performancegenetics.com) to three parts instead of the anticipated two, in order to give the detailed explanations that are required. In this Part II, Rogers explains in detail about how he has used video and image recognition to create his predictive models. Part I can be accessed online (www.internationalthoroughbred.co.uk) and in that article Rogers explains how he has brought together a data set in order for the computer to categorise the elite and non-elite racehorse.
base models for our tabular Biomechanics model. Each base model is trained on the same dataset and then we score all the records in the database and create a second dataset, which is just the probabilities from each of the base models. The second dataset then produces the overall prediction. But let’s rewind a little in order to explain the elements of the base models and how they are used.
The Biomechanics Model
The tabular Biomechanics model is our most predictive model, mainly because it is trained on the largest dataset and it uses five different base models to generate the data to make a prediction. Let’s walk (no pun intended) through the process.
We start by clipping a 10-second raw video of the horse walking left to right. It is important to get a good representation of the horse so we want to see it walk as best we can. Once we have the video, the first process that is undertaken when it is loaded into the application is what is known as “Embedding Clustering”. In machine learning, embeddings are a type of feature representation that can be used to represent data as vectors. To explain, let’s say we have a dataset of images of different flowers. We can use a deep-learning model to learn the embeddings for these images. Once we have the embeddings, we can then use a process called “k-means clustering” to group the images together so that all the roses are in the one cluster, while all the pansies are in another.
