Machine Learning Models
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 clusters. 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 that 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 and the Data Intelligence Platform
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
FAQ
Machine learning models are algorithms that learn patterns from data to make predictions, classifications, or decisions without being explicitly programmed. They are the core of AI systems used in analytics, automation, and predictive modeling.
Machine learning models are important because they help businesses and organizations automate decision-making, uncover insights from large datasets, improve efficiency, and predict future trends with high accuracy.
The main types of machine learning models include supervised learning (trained on labeled data), unsupervised learning (identifies patterns in unlabeled data), and reinforcement learning (learns through trial and error to maximize outcomes).
Machine learning models work by analyzing historical data to identify patterns and relationships. The model is then trained to make predictions or decisions on new, unseen data, and its performance is evaluated and refined for accuracy.
Machine learning models enable predictive analytics, personalized recommendations, fraud detection, process automation, and improved decision-making. They help organizations handle complex data and uncover insights that are difficult to detect manually.
Successful implementation requires collecting high-quality data, choosing the right algorithms, training and testing models rigorously, monitoring model performance, and integrating the models into business processes for actionable insights.
