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Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Predictive analytics, or predictive AI, encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
The significantly reorganized revised edition of the book (2023) [2] expands and modernizes the presented mathematical principles, computational methods, data science techniques, model-based machine learning and model-free artificial intelligence algorithms. The 14 chapters of the new edition start with an introduction and progressively build ...
Chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. 13 properties of each wine are given 178 Text Classification, regression 1991 [196] [197] M. Forina et al. Combined Cycle Power Plant Data Set Data from various sensors within a power plant running for 6 years. None 9568 Text Regression 2014
I've seen a ton of AI-generated text, made by the best large language models (LLMs) with expertly crafted prompts and highly specific, high-quality data. The results are still repetitive, clumsy ...
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