Machine learning models use algorithms to find patterns in data and make predictions. However, the way a model learns can be influenced by various settings that control its behavior. These are known as hyperparameters, choosing appropriate values for them has a...

Machine learning models often work with datasets containing many different variables, these are known as features or attributes. Although they provide useful information, not all of them are equally relevant to a specific task. Having too many features increases...

The goal of machine learning models is to identify patterns in data and use them to make predictions or uncover relationships. However, building a model is only part of the process. It’s also important to determine how well the model performs and whether its results...

Machine learning models are designed to study patterns from data and use them to make predictions or identify relationships. However, before a model can be used in real world applications, it needs to be trained and tested. These two stages help determine whether a...

When building a machine learning model, it’s important for the model to learn useful patterns from the data without becoming too simple or closely focused on the examples it has already seen. Two common problems that can occur are known as overfitting and...

MSET SP​RT is a machine learning approach designed to detect subtle anomalies in systems that are monitored over time. Its full name stands for Multivariate State Estimation Technique and Sequential Probability Ratio Test. Instead of simply looking for unusual...

Linear Regression is one of the most widely used approaches for predicting numerical values. It works by examining the relationship between one or more input variables and a numerical outcome, then uses those relationships to make predictions. Because of its fairly...

Logistic Regression is a supervised machine learning algorithm used to predict the probability of an outcome belonging to a particular category. Unlike ordinary regression, which is generally used to predict numerical values, Logistic Regression is designed for...

Ridge Regression is a machine learning algorithm used to predict numerical outcomes while assisting with situations where input variables are strongly related to one another. This can be a problem in traditional regression due to the highly correlated variables that...

It's often difficult to pinpoint the parts of the data which are most useful when analyzing datasets. A machine learning technique that simplifies this issue by identifying important rows and columns within a dataset is CUR Matrix Decomposition. Like in Oracle ML, it...

Oftentimes, it isn’t very obvious how the different records in datasets should be grouped. There may be patterns inside the data, but no predefined categories to tell us where each record belongs. Expectation Maximization or EM for short, is an unsupervised machine...

Not all machine learning problems are about predicting a known outcome. Sometimes the more interesting question is whether something looks unusual when compared with everything else. This is the idea behind One Class Support Vector Machine (One Class SVM). Supported...

Large datasets can contain a huge amount of information, this does not always make analysis easier. In many cases, the challenge is finding a simpler way to represent the data while keeping the patterns that actually matter. Non Negative Matrix Factorization (NMF) is...

Finding significant data groups within datasets can be a challenge, especially when there are no predefined categories to work with. O Cluster, short for Orthogonal Partitioning Clustering, is an unsupervised machine learning algorithm developed by Oracle to...

Understanding text can be a difficult task for computers. While people can easily recognize the meaning behind words and phrases, computers need methods that assists them interpret language in a meaningful way. Explicit Semantic Analysis, also referred to as ESA, is a...

Singular value decomposition is an unsupervised machine learning technique used primarily for feature extraction and the transformation of high dimensional data into a lower dimensional space whilst keeping meaningful data properties (dimensionality reduction). When...

As datasets continue to grow in size and complexity, analyzing every variable can quickly become challenging. Many datasets contain hundreds or even thousands of features, some often provide similar information. Principal Component Analysis, also known as PCA, is an...

When working with large amounts of data, one of the hardest challenges is not knowing where to start. Data can contain thousands to millions of records, and without clear labels, it can be difficult to understand how those records relate to one another. K-Means...

When analyzing large datasets, some of the most valuable insights come from discovering relationships that might otherwise go unnoticed. This is where the Apriori algorithm comes in. Supported within Oracle Machine Learning, it is an unsupervised algorithm designed to...

When people think about machine learning, they often focus on advanced algorithms such as neural networks or random forests. However, some of the most useful predictive models are built upon well established statistical techniques. One example is the Generalized...

Support Vector Machine (SVM) is a supervised machine learning algorithm that can be applied to both classification and regression problems. Its purpose is to detect patterns within data and make predictions by identifying the most effective boundary between different...

Exponential Smoothing is a forecasting algorithm used to analyze time based data and predict future values. It’s particularly useful when data is collected over regular intervals, such as daily sales figures, monthly revenue, or weekly demand. The algorithm works by...