Data Science & Machine Learning – C5.0 Decision Tree Use Case – DIY- 26 -of-50

Data Science & Machine Learning – C5.0 Decision Tree Use Case – DIY- 26 -of-50
Do it yourself Tutorial
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Bharati DW Consultancy
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C5.0 Decision Tree – Classification
C50_modelanglebrace- C5.0(train_Predictors, train_Target)
C50_predictanglebrace- predict(C50_model, test_data)

Get the data from Balance Scale Data Set.

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”

Data Science & Machine Learning – Getting Started – DIY- 1 -of-50
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Data Science & Machine Learning – C5.0 Decision Tree Use Case – DIY- 26 -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,