Categorical data is a type of data that can be divided or classified into groups. Understand the definition and examples of categorical data, learn to distinguish categorical data from quantitative data, and explore the uses of categorical data. ...
Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount...
data sets and over 600 exercises; notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources; and, a supplementary website showing how to use R and SAS; for all examples in the ...
Indrajit Saha, Dariusz Plewczyński, Ujjwal Maulik;Sanghamitra B;yopadhyay - International Conference on Rough Sets & Current Trends in Computing 被引量: 10发表: 2010年 An Introduction to Categorical Data Analysis (2nd ed.) Alan Agresti is well known for his concise explanations and his clea...
We illustrate this with several examples.doi:10.1007/s10618-005-0010-xStephen E. FienbergAleksandra B. SlavkovicSpringer USData Mining & Knowledge DiscoveryFienberg, S. and Slavkovic, A. Preserving the confidentiality of categorical statistical data bases when releasing information for association rules...
Count data are discrete and quantitative. Qualitative data tend to be categories; people are male or female, European, American, or Japanese, and they have a disease or are in good health. These are examples of categorical data. There are four types of scales that appear in social sciences:...
2.2.4Data Types and Conversion The attributes in a data set can be of different types, such as continuous numeric (interest rate), integer numeric (credit score), or categorical. In some data sets, credit score is expressed as ordinal or categorical (poor, good, excellent). Differentdata mi...
A potential near-optimal solution for a multi-query graph matching problem is analyzed based on the maximal correspondence of query graphs and minimal isomorphic data graphs (Section 3). A category-driven method is proposed to solve the problem of multi-query graph matching. Firstly, the query ...
applied fields. Well supplemented with helpful graphs and tables, Applied Categorical Data Analysis: Covers both basic and advanced topics Employs many real-life examples from biomedicine, epidemiology, and public health Presents case studies in meticulous detail Provides end-of-chapter exercise sets and...
y_label_encoded = final_label_encoded['price']# Split the data into train and test sets for One-Hot EncodingX_train_one_hot, X_test_one_hot, y_train_one_hot, y_test_one_hot = train_test_split(X_one_hot, y_one_hot, test_size=0.2, random_state=42)# Split the data into train...