Prediction in machine learning refers to the output of an algorithm after it has been trained on a historical dataset and applied to new data when forecasting the likelihood of a particular outcome.
Prediction in machine learning is commonly used for security, marketing, operations, risk, and fraud detection.
Here are just a few examples of how predictive analytics is utilized in different industries:
Predictive analytics in the banking and financial services industry are used in conjunction to detect and reduce fraud, measure market risk, and identify new business opportunities.
Predictive analytics and machine learning play a critical role in security. Security institutions typically use predictive analytics to improve services and performance, but also to detect anomalies, fraud, understand consumer behavior and enhance data security.
Predictive analytics and machine learning allow retailers to better understand consumer behavior, such as who will buy what and at what store? These questions can be readily answered with the right predictive models and data sets, helping retailers to plan ahead and stock items based on seasonality and consumer trends.
Predictions in machine learning allow businesses to make an accurate assumption as to the likely outcome of a question based on historical data. These predictions give businesses insights that result in tangible business value. For example, with churn, if a model predicts a customer is likely to churn, the business can target them with specific communications and outreach that can help prevent the loss of that customer.
Machine learning increases the speed at which data is processed and analyzed. With machine learning, predictive analytics algorithms can train on even larger data sets and perform deeper analysis on multiple variables with minor changes in deployment.
Common predictive models include:
Decision trees are produced by algorithms that identify various ways of splitting data into branch-like segments. Decision trees partition data into subsets based on categories of input variables. This helps you to understand someone’s path of decisions.
Regression (linear and logistic)
Regression analysis estimates relationships among variables, finding key patterns in large and diverse data sets and how they relate to each other.
Patterned after the neurons in the human brain, neural networks are a variety of deep learning technologies. Neural networks are typically used to solve complex pattern recognition problems. They work well when handling nonlinear relationships in data – and work well when certain variables are unknown.
H2O.ai and Prediction: With H2O-3, you can generate predictions for a model based on samples in a test set using h2o.predict() or predict(). This can be accomplished in memory or using MOJOs/POJOs.
Classification focuses on separating data into classes, prediction focuses on fitting a shape that gets as close to the data as possible.
Prediction and inference are two different areas of machine learning. Prediction is the ability to accurately guess a response variable while inference focuses on understanding a relationship between predictor variables and response variables.
Prediction is the process of a model-making prediction about something that has not yet happened. Inference focuses on evaluating the relationship between the predictor and response variables.