Clustering, Data Classification and Decision Trees

Predictive modeling

Dataconomy

MARCH 17, 2025

Unsupervised models Unsupervised models typically use traditional statistical methods such as logistic regression, time series analysis, and decision trees. These methods analyze data without pre-labeled outcomes, focusing on discovering patterns and relationships.

Decision Trees

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Five machine learning types to know

IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms —predict categorical output variables (e.g., “junk” or “not junk”) by labeling pieces of input data. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.

Machine Learning

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Ever wonder what makes machine learning effective?

Dataconomy

AUGUST 31, 2023

The goal of unsupervised learning is to identify structures in the data, such as clusters, dimensions, or anomalies, without prior knowledge of the expected output. This can be useful for discovering hidden patterns, identifying outliers, and reducing the complexity of high-dimensional data.

Machine Learning

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Predictive modeling

Five machine learning types to know

Ever wonder what makes machine learning effective?

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