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 91

Decision Trees Predictive Analytics Data Preparation Machine Learning 91

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 92

Machine Learning Machine Learning Supervised Learning Clustering 92

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 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Mlearning.ai

NOVEMBER 29, 2023

Decision Trees ML-based decision trees are used to classify items (products) in the database. In its core, lie gradient-boosted decision trees. This means that it is best used for elaborating data classifications in conjunction with other efficient algorithms.

Machine Learning 52

Machine Learning Machine Learning ML ML 52