Machine Learning for Knowledge Discovery with R Methodologies for Modeling, Inference and Prediction
Auteur : Tsai Kao-Tai
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.
Key Features:
- Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies.
- Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations.
- Written by statistical data analysis practitioner for practitioners.
The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.
Kao-Tai Tsai obtained his Ph.D. in Mathematical Statistics from University of California, San Diego and had worked at AT&T Bell Laboratories to conduct statistical research, modelling, and exploratory data analysis. After that, he joined the US FDA and later pharmaceutical companies focusing on biostatistics, clinical trial research and data analysis to address the unmet needs in human health.
Date de parution : 09-2023
15.6x23.4 cm
Date de parution : 09-2021
15.6x23.4 cm
Thèmes de Machine Learning for Knowledge Discovery with R :
Mots-clés :
TCGA; neural networks; Sparse Group Lasso; support vector machines; Random Forest; clustering; SVM; Scad; Group Lasso; Local Odds Ratio; Elastic Net; Propensity Score; Ridge Regression; Generate Propensity Scores; TCGA Data; Adaptive Lasso; Recursive Partitioning; Elastic Net Estimator; MLP; Optimal Hyperplane; Scatter Plot Matrix; OOB Error Rate; Ward's Minimum Variance Clustering; UCI Machine Learn Repository; Support Vector Regression Approach; Scatter Plot; Cumulative Link Models; Model Based Cluster Analysis