Data Science & Machine Learning – C5.0 Decision Tree Intro – DIY- 25 -of-50

Do it yourself Tutorial

by

Bharati DW Consultancy

cell: +1-562-646-6746 (Cell & Whatsapp)

email: bharati.dwconsultancy@gmail.com

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Google Drive-

C5.0 Decision Tree – Classification

Decision trees are very powerful classifiers, which utilize a tree structure to model the relationships among the features and the potential outcomes.

An all-purpose classifier which has a highly automatic learning process; it can handle numeric or nominal features.

C5.0 uses entropy, a concept analogous to the information theory that quantifies the randomness, or disorder, within a set of class values.

C50_modelanglebrace- C5.0(train_Predictors, train_Target)

C50_predictanglebrace- predict(C50_model, test_data)

Get the data from Balance Scale Data Set.

Attribute Information:

Class Name: 3 (L, B, R)

Left-Weight: 5 (1, 2, 3, 4, 5)

Left-Distance: 5 (1, 2, 3, 4, 5)

Right-Weight: 5 (1, 2, 3, 4, 5)

Right-Distance: 5 (1, 2, 3, 4, 5)

Citation Policy:

If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository:

Lichman, M. (2013). UCI Machine Learning Repository []. Irvine, CA: University of California, School of Information and Computer Science.

Here is a BiBTeX citation as well:

@miscLichman:2013 , author = “M. Lichman”,

year = “2013”, title = “UCI Machine Learning Repository”,

url = “”, institution = “University of California, Irvine, School of Information and Computer Sciences”

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Data Science & Machine Learning – R Data Structures – Factors – DIY- 3 -of-50

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Data Science & Machine Learning – Regression Decision Trees contd – DIY- 15 -of-50

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Data Science & Machine Learning – C5.0 Decision Tree Intro – DIY- 25 -of-50

Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree,