Weka

TÉCNICAS DE ANÁLISIS DE DATOS

5.8. part en weka 221 5.9. naive bayesiano en weka 222 5.10. vfi en weka 223 5.11. knn en weka (ibk) 224 5.12. k* en weka 226 5.13. redes de neuronas en weka 227 5.14. regresiÓn lineal en weka 228 5.15. regresiÓn lineal ponderada localmente en weka 230 5.16. m5 en weka 231 5.17. kernel density en weka 232 5.18. k-means en weka 234 5.19 ...

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DATA ANALYSIS WITH WEKA - MERI

WEKA is an open source application that is freely available under the GNU general public license agreement. Originally written in C the WEKA application has been completely rewritten in Java and is compatible with almost every computing platform. It is user friendly with a graphical interface that allows for quick set up and operation.

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An Introduction to the WEKA Data Mining System

Weka is a landmark system in the history of the data mining and machine learning research communities, because it is the only toolkit that has gained such widespread adoption and survived for an extended period of time (the first version of Weka was released 11 years ago). Other data mining and machine learning

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MANUAL DE WEKA - Universidad de Salamanca

Además Weka contiene las herramientas necesarias para realizar transformaciones sobre los datos, tareas de clasificación, regresión, clustering, asociación y visualización. Weka está diseñado como una herramienta orientada a la extensibilidad por lo que añadir nuevas funcionalidades es una

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Lab Exercise 1 Association Rule Mining with WEKA

1 Lab Exercise 1 Association Rule Mining with WEKA Association Mining is defined as finding patterns, associations, correlations, or casual structures among sets of items or objects in transaction dataset, relational database, and other information

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รายงานการวิจัย เรื่อง

management system and using Weka 3.6.2 to verify the accuracy of the mode. The result showed that algorithm decision tree ID3 built the best model with the highest percentage of correctly classified instances rate was 92.3 (tested the model with …

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Linear Regression and Support Vector Regression

WEKA (Linear Regression) Performance = (72.8 x MYCT) + (484.8 x MMIN) + (355.6 x MMAX) + (161.2 x CACH) + (256.9 x CHMAX) –53.9 Large Machine cycle time (MYCT) does not indicate the best performance Main memory plays a more important role in the system performance

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Predicting Diabetes in Medical Datasets Using Machine ...

WEKA works best than Matlab and Rapidminner. Lee . et al. [4] focus on applying a decision tree algorithm named as CART on the diabetes dataset after applying the resample filter over the data. The author emphasis on the class imbalance problem and the need to handle this problem before applying any algorithm to achieve better accuracy rates.

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C4.5 Decision Tree Algorithm - University of Houston

Simplified Algorithm Let T be the set of training instances Choose an attribute that best differentiates the instances contained in T (C4.5 uses the Gain Ratio to determine) C d h l hh bCreate a tree node whose value is the chosen attribute Create child links from this node where each link represents a unique value for the chosen attribute