What are Algorithms?
Have you ever followed a recipe? How about a flowchart for decision-making (if this, then that; if not, then this)? That’s basically what a machine learning algorithm is to a computer.
Algorithms can be thought of as step-by-step computational procedures for solving a problem, almost like a road map for accomplishing a task.
You’ve probably heard the term in reference to the way Facebook and Google determine which posts and advertisements to display to different users, but algorithms are also used by businesses across the economy every day for information processing, mathematical calculation, and other related operations.
Machine learning and data science rely on algorithms to produce predictive analytics models that reveal patterns, which in turn allow businesses to make predictions based on historical data. There are many different algorithms, but most data scientists rely on the ones with which they are most familiar, which limits the level of accuracy their predictions are able to achieve.
Why are Algorithms important?
Algorithms are a central part of any computation: they define how the computer should operate and how exactly it should solve a problem. Data scientists and mathematicians often use complex algorithms as building blocks for more efficient logical problem-solving, and we use algorithms in our day to day lives for checking email, listening to music, and much more.
Solving real-world business problems with machine learning and data science requires advanced algorithms, which take a lot of time and skill to work with – but without them, we wouldn’t even have basic math, let alone be able to predict which families are best suited to become foster parents.
Algorithms + DataRobot
With traditional data science methods, running a single algorithm can be prohibitively difficult and time-consuming – not to mention the work that goes into complicated and technical data science techniques like feature engineering and model tuning. DataRobot automates the model building process and runs dozens of models in parallel, simultaneously expanding the number of models you are able to run and cutting down the time it takes to run them (that’s a productivity increase of about 10 times – just ask Trupanion).