Category: Analytics

One of the pitfalls of machine learning is that creating a single predictive model has the potential to overfit your data. That is, the performance on your training data might be very good, but the model does not generalize well to new data. Ensemble learning of decision trees, also referred to as forests or simply ensembles,  is a tried-and-true technique for reducing the error of single machine-learned models. By learning multiple models over different subsamples of your data and taking a majority vote at prediction time, the risk of overfitting a single model to all of the data is mitigated. You can read more about this in our previous post.

Early this year, we showed how BigML ensembles outperform their solo counterparts and even beat other machine learning services. However, up until now creating ensembles with BigML has only been available via our API. We are excited to announce that ensembles are now available via our…

This is a guest post by David Gerster (@gerster), a data scientist and investor in BigML.

I work at a consumer web company, and recently used BigML to understand what drives return visits to our site. I followed Standard Operating Procedure for data mining, sampling a group of users, dividing them into two classes, and creating several features that I hoped would be useful in predicting these classes. I then fed this training data to BigML, which quickly and obediently produced a decision tree:

Next I used BigML’s interface to examine the tree’s many subsets, shown as “nodes” in the diagram above. I moused over a node at the top of the tree and saw that it achieved high separation for a large fraction of the training set:

This one node covered 58% of the data, and separated the two classes with 73% confidence. (“Confidence” is a measure…