UID:
almahu_9949385333302882
Format:
1 online resource :
,
illustrations (black and white)
Edition:
1st.
ISBN:
9781000450354 (ePub ebook)
,
100045035X (ePub ebook)
,
9781000450279 (PDF ebook)
,
1000450279 (PDF ebook)
,
9781003205685 (ebook)
,
1003205682 (ebook)
Content:
"Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein"--
Note:
1. Statistical Data Analysis. 2. Examining Data Distribution. 3. Regression with Shrinkage. 4. Recursive Partitioning Modeling. 5. Support Vector Machines. 6. Cluster Analysis. 7. Neural Networks. 8. Causal Inference and Matching. 9. Business and Commercial Data Modeling. 10. Analysis of Response Profiles.
Additional Edition:
Print version: ISBN 9781032065366
Language:
English
Keywords:
Electronic books.
DOI:
10.1201/9781003205685
URL:
https://www.taylorfrancis.com/books/9781003205685
