What is a Model?
That’s a big question.
Generally speaking, a model is a representation of something else: architects make miniature models of building designs, fashion models wear clothes so you can see how they’ll look, and statistical or mathematical models represent relationships between and among data.
In machine learning and data science, the word “model” is pervasive. Statistical and mathematical models have multiple purposes, ranging from descriptive to predictive to prescriptive analytics. Essentially, the goal of developing models in machine learning is to extract insights from data that you can use to make better business decisions.
In predictive analytics, algorithmic models tell you which outcome is likely to hold true for your target variable based on your training data. It constructs a representation of the relationships and teases out patterns between all the different features in your dataset, from which you can make predictions on similar data you collect in the future. It’s more abstract than an architectural model, but it’s the same idea: a distilled representation of a greater picture.
Why are Models important?
This one is kind of self-explanatory. Models form the basis of every type of data analysis we do; without them, we would still be stuck at simple computation (1+2=3). No statistical models? No way to determine relationships. No predictive models? No way to make predictions from data. It’s as simple as that.
Models + DataRobot
DataRobot is the name, but models are the game. The DataRobot automated machine learning platform automates the model building process, eliminating most of the manual math and coding data scientists used to have to do to produce them – which means that anyone, regardless of data science expertise, can build practical predictive models that have tangible effects on their business’s bottom line.
The DataRobot platform runs what we call “model blueprints,” which are meta-models. The blueprints contain the machine learning algorithm model, but they also contain a combination of data pre-processing, feature engineering, and post-processing steps that result in more relevant predictions for your business problem. Its built-in guardrails and baked-in best practices from the minds of some of the best data scientists around make sure that its models are as accurate and practical as possible. Not only that, its parallel processing capability means it produces many models all at once, increasing the productivity and return on investment of data science initiatives by tenfold.
DataRobot also has tools to increase the interpretability of machine learning models it creates, allowing business and domain experts to more easily communicate their insights to those who have the final say as to whether or not those models actually get deployed into production.
If that sounds too good to be true, think again – or request a demo so we can prove it to you.