Machine Learning Modeling

Smiling professional woman with a tablet, possibly studying machine learning modeling

Machine learning involves selecting algorithms and training computer models to uncover data patterns or make predictions based on training data.

Why is Machine Learning Modeling Important?

Machine Learning (ML) can process vast amounts of data to uncover subtle correlations, discover new insights and provide more accurate predictions than would be practical using traditional methods. Machine learning models can be trained unsupervised or guided with human input to produce the best results.

Machine learning is an efficient way to scale scarce data science and data engineering resources. Once trained, a machine learning model can analyze data streams as new data is created to provide real-time insights which help an organization respond to market and customer behavior changes as they happen.

What are Machine Learning Models?

There are many kinds of machine learning models adapted to different needs. Below is a selection of models.

Linear Regression Models

Linear regression models predict values based on historical data. The model looks for patterns in a data stream that it uses to project forwards.

Decision Tree Models

Decision trees are well suited to classification tasks, making recommendations and detecting anomalies. For a binary decision tree, the outcome is either Yes or No. A series of variables are considered branches, resulting in one of the two outcomes.

K-Nearest Neighbors Model

In this classification model, data is sorted, and the evaluated value is placed in the same class as its nearest neighbors.

Neural Network Models

A neural network mimics the human brain with nodes where data intersects and connections in a network lattice. The training data teaches the network how to respond to certain stimuli. For an autonomous driving application, the vehicle will do what humans have been recorded doing when responding to potholes, road signs or standing water, for example.

Logistic Regression Model

The logistic regression model aims to arrive at a yes or no answer, which helps predict customer behavior or anticipate customer churn. Linear discriminant analysis is a branch of the logistic regression model that can average results from many decision trees to perform facial or speech recognition.

Naive Bayes Model

Naive Bayes is used for tasks such as detecting spam emails and categorizing emails.

Transformer Model

Chat GPT uses a transformer-type neural network to track relationships in a learning context. It uses this knowledge to change an input sequence into an output sequence.

Machine Learning Modeling Training

There are four common types of machine learning:

  1. Supervised machine learning algorithms where humans provide examples of good outcomes.
  2. Unsupervised machine learning algorithms allow algorithms to find correlations in the data.
  3. Semi-supervised machine learning algorithms provide some human input.
  4. Reinforcement machine learning algorithms have humans guide the model by providing feedback on output to reinforce correct behavior.

How is Machine Learning Applied?

The following are examples of applications that use machine learning.

Real-Time Analytics

Machine learning models can study events such as weather and social media streams to determine if a situation is likely to escalate and advise operators.

Online Retail

Machine Learning models can personalize shopping by providing real-time recommendations and running relevant promotions.


Doctors can use AI models to diagnose problems and get advice on treatments.

Stock Trading

Machine Learning models can provide buy and sell guidance based on trading patterns, SEC filings and news about a company.

Risk and Fraud Management

Credit card issuers and insurance companies have to monitor for fraud continuously. AI models enable them to study transactions as they happen to predict which ones are suspicious.

Actian Data Management for Machine Learning Projects

Machine learning models rely on sound data to make accurate predictions. The Actian Data Platform perfectly complements ML projects by providing a unified experience for ingesting, transforming, storing and analyzing data.

Built-in data integration technology prepares training data by automating data pipelines that prepare data for training machine learning models.

The Actian Data Platform is available on-premise and on multiple public cloud platforms.

Try the Actian Data Platform 30-day free trial here.