Practical Data Science with R

List of Tables

Chapter 1. The data science process

Table 1.1. Data science project roles and responsibilities

Table 1.2. Loan data attributes

Chapter 3. Exploring data

Table 3.1. Visualizations for one variable

Table 3.2. Visualizations for two variables

Chapter 5. Choosing and evaluating models

Table 5.1. Some common classification methods

Table 5.2. From problem to approach

Table 5.3. Ideal models to calibrate against

Table 5.4. Standard two-by-two confusion matrix

Table 5.5. Example classifier performance measures

Table 5.6. Classifier performance measures business stories

Table 5.7. Common model problems

Chapter 7. Linear and logistic regression

Table 7.1. Some variables in natality dataset

Chapter 8. Unsupervised methods

Table 8.1. ...