Practical machine learning in R - Nwanganga, Frederick Chukwuka; Chapple, Mike, 1975- (London : ISTE, Ltd. ; Hoboken : Wiley, 2020)
Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning'a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions'allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms.' Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more.' -Explores data management techniques, including data collection, exploration and dimensionality reduction -Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering -Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques -Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.
Influential Point Analysis -- Multicollinearity -- Improving the Model -- Considering Nonlinear Relationships -- Considering Categorical Variables -- Considering Interactions Between Variables -- Selecting the Important Variables -- Strengths and Weaknesses -- Case Study: Predicting Blood Pressure -- Importing the Data -- Exploring the Data -- Fitting the Simple Linear Regression Model -- Fitting the Multiple Linear Regression Model -- Exercises -- Chapter 5 Logistic Regression -- Prospecting for Potential Donors -- Classification -- Logistic Regression -- Odds Ratio
Print version record.
Binomial Logistic Regression Model
John Wiley and Sons
Keywords: Machine learning; R (Computer program language); Apprentissage automatique; R (Langage de programmation); COMPUTERS -- Software Development & Engineering -- General; Machine learning; R (Computer program language); 2; 2; 2