May 2020
Intermediate to advanced
530 pages
17h 8m
English
Content preview from Hands-On Machine Learning with C++
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Ensembles with Shark-ML
There is only one ensemble learning algorithm in the Shark-ML library, which is the random forest, and it can be trained only for solving classification tasks. So, for this set of samples, we will use the Breast Cancer Wisconsin (Diagnostic) dataset located at https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). It is taken from Dua, D. and Graff, C. (2019) UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science: http://archive.ics.uci.edu/ml.
There are 569 instances in this dataset, and each instance has 32 attributes: the ID, the diagnosis, and 30 real-value input features. The diagnosis can have two values: M = malignant, and B
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