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Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 6, Trees and Rules of Data Mining by I. H. Witten, E. Frank, M. A. Hall and C. J. Pal. Algorithms for learning trees and rules. Decision trees From ID3 to C4.5 (pruning, numeric attributes, ...)

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846 views • 57 slides
Beam Current Monitors Lunch-Time Seminar

Beam Current Monitors Lunch-Time Seminar

Beam Current Monitors Lunch-Time Seminar. Martin Kesselman April 12, 2001. WHAT DO WE WANT TO MEASURE?. Number of charged particles in the beam A non-destructive measurement Measure current Number of charged particles per unit time Integrate over total beam pulse to get total charge. S.

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433 views • 27 slides
5-Slide Example: Gene Chip Data

5-Slide Example: Gene Chip Data

5-Slide Example: Gene Chip Data. Decision Trees in One Picture. Example: Gene Expression. Decision tree: AD_X57809_at <= 20343.4: myeloma (74) AD_X57809_at > 20343.4: normal (31) Leave-one-out cross-validation accuracy estimate: 97.1% X57809: IGL (immunoglobulin lambda locus).

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344 views • 26 slides
Data Mining Schemes in Practice

Data Mining Schemes in Practice

Data Mining Schemes in Practice. Implementation: Real machine learning schemes. Decision trees: from ID3 to C4.5 missing values, numeric attributes, pruning efficiency Instance-based learning Speed up, combat noise, attribute weighting, generalized exemplars. Numeric attributes.

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319 views • 19 slides
Decision Trees with Numeric Tests

Decision Trees with Numeric Tests

Decision Trees with Numeric Tests. Industrial-strength algorithms. For an algorithm to be useful in a wide range of real-world applications it must: Permit numeric attributes Allow missing values Be robust in the presence of noise

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298 views • 18 slides

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