Overfitting is a fundamental issue in supervised machine learning which prevents us from
perfectly generalizing the models to well fit observed data on training data, as well as …

Microbiome data predictive analysis within a machine learning (ML) workflow presents
numerous domain-specific challenges involving preprocessing, feature selection, predictive …

Despite the popularity of unsupervised techniques for political science text-as-data research,
the importance and implications of preprocessing decisions in this domain have received …

The field of machine learning has matured to the point where many sophisticated learning
approaches can be applied to practical applications. Thus it is of critical importance that …

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for
Business introduces the fundamental principles of data science, and walks you through the" …

Recursive binary partitioning is a popular tool for regression analysis. Two fundamental
problems of exhaustive search procedures usually applied to fit such models have been …

Feature selection is applied to reduce the number of features in many applications where
data has hundreds or thousands of features. Existing feature selection methods mainly focus …

Business intelligence is a broad category of applications and technologies for gathering,
providing access to, and analyzing data for the purpose of hel** enterprise users make …

Data snoo** occurs when a given set of data is used more than once for purposes of
inference or model selection. When such data reuse occurs, there is always the possibility …

For large, real-world inductive learning problems, the number of training examples often
must be limited due to the costs associated with procuring, preparing, and storing the …