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...

Naive Bayes is a supervised machine learning algorithm consisting of three main types (Gaussian, Multinomial & Bernoulli). This probability-based algorithm is mainly used for classification tasks by using the relationships between different features to predict the...

Random Forest is a supervised machine learning algorithm used for both classification and regression tasks. It is based on the idea of combining multiple decision trees to produce a stronger and more reliable model. Instead of relying on a single tree, Random Forest...

Minimum Description Length, often shortened to MDL, is a principle used in machine learning and data analysis to help identify models that best explain a dataset while avoiding unnecessary complexity. The main idea behind MDL is that the best model is one that...

Neural networks are a machine learning technique inspired by the way the human brain processes information. They are designed to recognize patterns, learn from data, and make predictions by analyzing relationships between inputs and outputs. Neural networks are...

Decision trees are a supervised machine learning technique used for both classification and regression tasks. They work by breaking down a problem into a series of simple decisions, creating a tree like structure of rules that lead to a final outcome. Each step in the...

XGBoost, short for Extreme Gradient Boosting, is a powerful machine learning algorithm used mainly for supervised learning tasks such as classification and regression. It is based on the idea of combining multiple simple models, typically decision trees, to create a...

Ranking is a machine learning technique used to order items based on their relevance, importance, or likelihood of a particular outcome. Rather than simply predicting a value or assigning a category, ranking focuses on prioritizing results so that the most useful or...

Row importance is a machine learning technique used to identify which individual records in a dataset are the most significant or influential. While many techniques focus on the importance of variables, row importance shifts the focus to the data points themselves. It...

Time series is a machine learning technique used to analyze and predict data that is collected over time. Unlike other approaches that treat data as independent observations, time series focuses on the order and timing of data points. This makes it especially useful...

Regression is a supervised machine learning technique used to predict continuous values based on patterns in data. Unlike classification, which assigns data into categories, regression focuses on estimating numerical outcomes. It looks at the relationship between...

Attribute importance is a machine learning technique used to determine which variables in a dataset have the greatest influence on a given outcome. While many datasets contain a large number of features, not all of them contribute equally to predictions. Attribute...

Clustering is an unsupervised machine learning technique that helps find natural groupings within a dataset. Instead of trying to predict a specific outcome, clustering focuses on identifying records that are similar to each other. Data points that share similar...

Association rules are a type of unsupervised machine learning technique that helps find relationships between items in a dataset. Instead of predicting a specific outcome, the goal is to spot patterns that show how certain items or events tend to occur together. These...

Anomaly detection is an unsupervised machine learning technique that identifies observations which differ significantly from most of the data. Unlike supervised methods such as classification or regression, anomaly detection does not need labelled outcomes. Instead,...

Previously, we discussed about what feature extraction is and the algorithms that support it. In this post, the focus shifts to why feature extraction is so useful in real world machine learning scenarios. Drawing from Oracle Machine Learning documentation, feature...

Machine learning projects are most successful when they follow a structured and repeatable process. Oracle Machine Learning adopts a well defined lifecycle that guides projects from the initial business problem through to deployment in a production environment. This...

An algorithm is a step-by-step procedure designed to perform a specific task or solve a certain problem. It involves a sequence of instructions that lead to the outcome. Supervised machine learning algorithms, on the other hand, are slightly different. These are, by...