Results 231 to 240 of about 1,670,948 (272)
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2021
Data analysis is the process of extracting insights from data. Data is heterogeneous in all ways, and processing such data is a challenge. Before applying any machine learning model to any datasets, it is necessary to understand the problem, deal with the missing values and noise, visualize the dataset, and to select the machine learning model to ...
Gitanjali Rahul Shinde +3 more
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Data analysis is the process of extracting insights from data. Data is heterogeneous in all ways, and processing such data is a challenge. Before applying any machine learning model to any datasets, it is necessary to understand the problem, deal with the missing values and noise, visualize the dataset, and to select the machine learning model to ...
Gitanjali Rahul Shinde +3 more
+6 more sources
2014
The role of an exploratory data analysis (EDA) is to equip the modeler with an understanding of the data. More specifically, an EDA helps to answer two core questions: (a) whether a trait is safety related and (b) what function can be used to represent it in the model equation.
+7 more sources
The role of an exploratory data analysis (EDA) is to equip the modeler with an understanding of the data. More specifically, an EDA helps to answer two core questions: (a) whether a trait is safety related and (b) what function can be used to represent it in the model equation.
+7 more sources
WIREs Computational Statistics, 2009
AbstractExploratory data analysis, or EDA for short, is a term coined by John W. Tukey for describing the act of looking at data to see what it seems to say. This article gives a description of some typical EDA procedures and discusses some of the principles of EDA.
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AbstractExploratory data analysis, or EDA for short, is a term coined by John W. Tukey for describing the act of looking at data to see what it seems to say. This article gives a description of some typical EDA procedures and discusses some of the principles of EDA.
openaire +2 more sources
2019
The main characteristics of multivariate data are discussed, and exploratory tools are provided to extract information from them. Different preprocessing strategies of normalization, smoothing and compression are described and compared for the cases of discrete and continuous variables.
José Manuel Díaz-Cruz +2 more
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The main characteristics of multivariate data are discussed, and exploratory tools are provided to extract information from them. Different preprocessing strategies of normalization, smoothing and compression are described and compared for the cases of discrete and continuous variables.
José Manuel Díaz-Cruz +2 more
openaire +2 more sources
An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
exaly