The next post in the BigML Machine Learning Throwdown.
Originally posted on The Official Blog of BigML.com:
In the third post of the series, we looked at the types of models supported by each service. While some are useful for understanding your data, the primary goal of many machine learning models is to make accurate predictions from unseen data. Say you want to sell your house but you don’t know how much it is worth. You have a dataset of home sales in your city for the past year. Using this data, you train a model to predict the sales price of a house based on its size and the year it was built. Will this model be useful for predicting how much your own house will sell for? In this post, I will discuss how a model’s prediction abilities are evaluated, the results of comparing models from each service, and some general observations about making predictions with each service.
As we saw in the previous post, some of the services report cross-validation scores on the models they create. These scores are a measure of a model’s ability to leverage what it learned from the training data to make accurate predictions from unseen data. A better score on your housing price model will give you more confidence that it will accurately predict the sales price of your house.