Machine Learning Model
What is a Model in Machine Learning?
Statistical and mathematical models have multiple purposes, ranging from descriptive to predictive to prescriptive analytics. The goal of developing models in machine learning is to extract insights from data that you can use to make better business decisions. Algorithmic models tell you which outcome is likely to hold true for your target variable based on your training data. They construct a representation of the relationships and tease out patterns between all the different features in your dataset that you can apply to similar data you collect in the future, allowing you to make decisions based on those patterns and relationships. 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?
Models form the basis of data analysis. Without them, we would be limited to simple computation (1+2=3). Without statistical models, there would be no way to determine relationships between variables, and without machine learning models, we wouldn’t be able to uncover relationships and gain insight from historical data.
Machine Learning Models + DataRobot
The DataRobot automated machine learning platform incorporates baked-in modeling techniques from top-ranked data scientists to automatically produce dozens of machine learning models with the click of a button. This enables anyone, regardless of data science expertise, to build practical models that have tangible effects on a business’s bottom line.
The DataRobot platform runs what we call “model blueprints,” meta-models that contain the machine learning algorithm in addition to a combination of data pre-processing, feature engineering, and post-processing steps. Model blueprints result in more relevant insights into your data, allowing you to make better decisions that yield tangible business value. Additionally, the platform’s 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.
DataRobot also has tools to increase the interpretability of machine learning models it creates, allowing users to more easily communicate insights from model outcomes to those who have the final say as to whether or not those models are deployed into production.