The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models.
A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.
In this book, you will work with the best Python tools to streamline your feature engineering pipelines, feature engineering techniques and simplify and improve the quality of your code.
non-pattern based sequence feature, 161 nonlinear time-series analysis, 96 online feature selection, ... 4, 251, 254 pattern selection, 253 pattern space, 262 People-Content-Network, 382 periodic sequence pattern, 158 PLSA, 33 Principal ...
Castaldi P, Dahabreh I, Ioannidis J (2011). “An Empirical Assessment of Validation Practices for ... Chambers J (2008). Software for Data Analysis: Programming with R. Springer ... Cohen G, Hilario M, Pellegrini C, Geissbuhler A (2005).
A perfect guide to speed up the predicting power of machine learning algorithms Key Features Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing ...
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used.
... 'SRG' for 'SVM-RFE-G', and 'IG' for 'Information Gain', respectively C4.5 NB k-NN SVM Dataset Best Acc. suc. ... 81.25 75 Rf,mRMR,Md,SR 100.00 100 FS-P 87.50 75 Rf,mRMR,Md,SR 75 SRG CorrAL-100 84.38 -13 W-C4.5 87.50 75 FS-P 90.63 99 ...
The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science.
This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate ...