Machine Learning Performance Improvement Cheat Sheet Photo by NASA, some rights reserved. This cheat sheet is designed to give you ideas to lift performance on your machine learning problem. Outcome: You should now have a short list of highly tuned algorithms on your machine learning problem, maybe even just one. In fact, you can often get good performance from combining the predictions from multiple "good enough" models rather than from multiple highly tuned (and fragile) models.
Please read on for my discussion of some of the limitations of the technique, and how we solve the problem for impact coding (also called "effects codes"), and a worked example in R.We define a nested model as any model where the results of a sub-model are used as inputs for a later model. And I now think such a theorem would actually have fairly unsatisfying statement as a one possible "bad real world data" situation violates the usual "no re-use" requirements of differential privacy; duplicated or related columns or variables break the Laplace noising technique. But library code needs to work in the limit (as you don't know ahead of time what users will throw at it) and there are a lot of mechanisms that do produce duplicate, near-duplicate, and related columns in data sources used for data science (one of the difference between data science and classical statistics is data science tends to apply machine learning techniques on very under-curated data sets). The results on our artificial "each column five times" data set are below: Notice that the Laplace noising technique test performances are significantly degraded (performance on held-out test usually being a better simulation of future model performance than performance on the training set).
Often the complexity a machine learning algorithms is in the model training, not in making predictions. I also strongly recommend gathering outlier and interesting cases from operations over time that produce unexpected results (or break the system). Like a ratchet, consider incrementally updating performance requirements as model performance improves. If you're interested in more information on operationalizing machine learning models check out the post: This is more on the Google-scale machine learning model deployment.
In this special guest feature, Victor Amin, Data Scientist at SendGrid, advises that businesses implementing machine learning systems focus on data quality first and worry about algorithms later in order to ensure accuracy and reliability in production. At SendGrid, Victor builds machine learning models to predict engagement and detect abuse in a mailstream that handles over a billion emails per day. The training set (the data your machine learning system learns from) is the most important part of any machine learning system. Instead, build a system that samples production data, and have a mechanism for reliably labeling your sampled production data that isn't your machine learning model.
At Criteo, machine learning lies at the core of our business. We use machine learning for choosing when we want to display ads as well as for personalized product recommendations and for optimizing the look & feel of our banners (as we automatically generate our own banners for each partner using our catalog of products). Our motto at Criteo is "Performance is everything" and to deliver the best performance we can, we've built a large scale distributed machine learning framework, called Irma, that we use in production and for running experiments when we search for improvements on our models. In the past, performance advertising was all about predicting clicks. That was a while ago.