- A machine learning prediction is the output of an algorithm trained on historical data, applied to new records.
- Prediction needs supporting infrastructure and high-quality data to succeed.
- Common uses include fraud detection, spam detection, churn prediction, cash-flow forecasting and healthcare diagnosis.
- Classification separates data into classes; prediction fits a shape as close to the data as possible.
- Machine learning can find patterns and relationships in large datasets that people cannot.
What is Machine Learning Prediction?
Machine learning prediction, or prediction in machine learning, refers to the output of an algorithm that has been trained on a historical dataset. The algorithm then generates probable values for unknown variables in each record of the new data. The purpose of prediction in machine learning is to project a probable data set that relates back to the original data. This helps organizations predict future customer behaviors and market changes. Essentially, prediction is used to fit a shape as closely to the data as possible. With machine learning predictions, organizations use proactive decisions to avoid predicted user churn. To gain the most success with prediction in machine learning, organizations need to have infrastructure in place to support the solutions, and high quality data to supply the algorithm.
Examples of Machine Learning Prediction
Prediction can be used to forecast the future and to predict the probability of an outcome. It can also be used to forecast future requirements or run a what-if analysis. One prediction tool is regression analysis which is used to determine the relationship between two variables (single regression) or more than two variables (multiple regression). Predictive analytics is when data is used to predict future trends or events. With predictive analytics, historical data is used to forecast potential scenarios and use these predictions to drive strategic business aimed decisions. Prediction can also be used to forecast future cash flow, determine staffing needs in the hospitality and entertainment industry, predict user behavior, prevent malfunctioning, and predict potential allergic reactions for patients in the healthcare industry.
Why is Machine Learning Prediction Important?
Prediction in machine learning allows organizations to make predictions about possible outcomes based on historical data.These assumptions allow the organization to make decisions resulting in tangible business results. Predictive analytics can be used to anticipate when users will churn or leave an organization. With this recognition, organizations have better potential to keep customers happy and satisfied.
Who Uses Prediction?
The prediction audience includes people who are in need of answers to future questions in order to make business decisions. Predictive analysis provides confident answers to complicated problems, explores new kinds of problems, and offers real time answers to problems with changing information. Prediction can be used by everyone because of its vast capabilities. It is helpful for businesses and other organizations when making decisions, but can also be used to make movie recommendations. Prediction is also used to detect fraud in previous transactions which is a function often used by banks.
Prediction vs Traditional Methods
Machine learning prediction is preferred over traditional methods because it is usually a better predictor. Since machine learning uses algorithms, it can identify patterns and relationships that humans cannot. Larger data sets are also able to be analyzed and turned into predictions. Traditional methods use humans for computation, which requires more time, money, and is subject to bias by human emotion or opinion. With users and the market constantly changing, machine learning prediction also provides the benefit of adapting quickly and higher efficiency.
Prediction vs classification: Classification is separating data into classes, whereas prediction is about fitting a shape that gets as close to the data as possible.
Prediction vs inference statistics: Prediction is the process of a machine learning model predicting potential data points. Inference statistics evaluate the difference between predictor and response variables.
Prediction in H2O AI
H2O Driverless AI, our AutoML, and open-source H2O-3 train models on historical data and score new records to produce predictions, as part of the H2O Predictive AI Platform.