Machine Learning Models

A team discussing data insights with Machine Learning Models displayed on a laptop screen in a bright office.

Machine Learning Models consume large volumes of data to make predictions, find correlations, or classify data. There are three machine learning methods: supervised learning, unsupervised learning, and reinforcement learning.

Why Are Machine Learning Models Important?

Machine Learning (ML) models enable businesses to extract more value from their available data assets. ML is a cost-efficient way to analyze data and find subtle correlations in data that humans might miss. Using ML, finding correlations in a dataset needs no supervision and does not require plotted data to visualize related data clustersata. Less technical users can benefit from complex data analysis output from ML models that traditionally require data scientists to interpret.

Machine Learning Model Types

Below are some examples of Machine Learning models.

Linear Regression Models

Linear regression is a predictive model that uses historical data to predict future data points. Being armed with computed likely outcomes increases the confidence of data-driven decisions.

Decision Tree Models

Decision tree models can be used for classification tasks and detecting anomalies in a dataset. Branches are created until an outcome is reached.

Logistic Regression Model

The logistic regression model is designed to arrive at a yes or no answer. This is useful for predicting customer churn, for example.

K-Nearest Neighbor Model

The k-nearest neighbor model is a supervised machine learning model used to classify tasks, diagnose patients, and make product recommendations.

Training Machine Learning Models

Unsupervised learning involves using an algorithm that understands how data may be correlated and providing a dataset to search for such correlations.

Supervised learning provides hints to the model of good and bad correlations with results in more accurate and unbiased results.

Machine Learning (ML) uses reinforcement learning from human feedback to improve accuracy. Humans capture and annotate model output to guide the model by rating outputs, for example.

Machine Learning Examples

Machine Learning can automate data analysis tasks across industries. Below are some example use cases.

Analyzing IoT Sensor Data Streams

The outputs from IoT devices such as cameras, scales, pressure and heat sensors can be fed to an ML model to assess whether a machine is running nominally or is likely to require maintenance or intervention soon. These data streams enable proactive maintenance to be scheduled.

Real-Time Analytics

Machine learning models can be loaded with data from social media or marketing campaigns, which can be used to predict selling opportunities or adverse business conditions. ML models can also use real-time data to indicate adverse events, such as forecasting extreme weather events.

Online Gaming

Sports betting and online poker back-end systems can use real-time data streams of player behavior and game statistics to accurately price bets and protect margins.

Retail

Machine learning models can use web activity coupled with past buying patterns to personalize digital ads and real-time promotions to increase customer engagement.

Healthcare

Doctors can use machine learning to assess the likelihood of specific outcomes dictated by thousands of similar cases in its training data.

Stock Trading

Stock traders are very data-driven, looking for reliable buying or selling signals. As profits and earnings for a company are positive, they invest and divest when the company enters a decline. Machine learning can increase risk scores as a stock’s PE ratio exceeds its industry peers.

Risk and Fraud Management

Financial institutions such as banks and credit card issuers must continuously monitor for fraudulent activities. Machine learning models monitor transactions as they occur to detect potential fraud by looking for anomalies. Any suspicious transactions undergo additional verification before they are authorized or declined.

Insurance companies also look for fraudsters who use subtle tactics only automated systems can detect as anomalous in real time. High-risk prospects are offered higher premiums, and low-risk customers are offered lower risk-adjusted premiums.

Actian Data Management for Machine Learning

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