Prediction

What does Prediction mean in Machine Learning?

“Prediction” refers to the output of an algorithm after it has been trained on a historical dataset and applied to new data when you’re trying to forecast the likelihood of a particular outcome, such as whether or not a customer will churn in 30 days. The algorithm will generate probable values for an unknown variable for each record in the new data, allowing the model builder to identify what that value will most likely be.

The word “prediction” can be misleading. In some cases, it really does mean that you are predicting a future outcome, such as when you’re using machine learning to determine the next best action in a marketing campaign. Other times, though, the “prediction” has to do with, for example, whether or not a transaction that already occurred was fraud. In that case, the transaction already happened, but you’re making an educated guess about whether or not it was legitimate, allowing you to take the appropriate action.

Why are Predictions important?

Machine learning model predictions allow businesses to make highly accurate guesses as to the likely outcomes of a question based on historical data, which can be about all kinds of things – customer churn likelihood, possible fraudulent activity, and more. These provide the business with insights that result in tangible business value. For example, if a model predicts a customer is likely to churn, the business can target them with specific communications and outreach that will prevent the loss of that customer.

DataRobot + Predictions

The DataRobot automated machine learning platform allows users to easily develop models that make highly accurate predictions. It streamlines the data science process so that users get high-quality predictions in a fraction of the time it used to take using traditional methods, allowing them to more quickly implement those predictions and start seeing the impact on their bottom line.

In order to start making predictions with DataRobot, you need to deploy the model into a production application. For more details, see the deployment wiki entry or the DataRobot model deployment briefing.