Predictive Models in Data Science

Amrina Ferdous; Jodi Mead

Predictive Models in Data Science

Amrina Ferdous, Jodi Mead

Mathematics Department

Boise State University

Research output: Contribution to conference › Presentation

Abstract

Predictive modeling in data science typically requires unknown parameters that can be inferred from observational data. A parameter is any numerical quantity that characterizes a given data set or some aspect of it. This study focuses on the effectiveness of combining data sets from different predictive models that share some common parameters. We will show preliminary results demonstrating the effect of uncertainties in initial parameter estimates inferred from a secondary data set.

Original language	American English
State	Published - 12 Apr 2019

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title = "Predictive Models in Data Science",

abstract = " Predictive modeling in data science typically requires unknown parameters that can be inferred from observational data. A parameter is any numerical quantity that characterizes a given data set or some aspect of it. This study focuses on the effectiveness of combining data sets from different predictive models that share some common parameters. We will show preliminary results demonstrating the effect of uncertainties in initial parameter estimates inferred from a secondary data set.",

author = "Amrina Ferdous and Jodi Mead",

year = "2019",

month = apr,

day = "12",

language = "American English",

}

TY - CONF

T1 - Predictive Models in Data Science

AU - Ferdous, Amrina

AU - Mead, Jodi

PY - 2019/4/12

Y1 - 2019/4/12

N2 - Predictive modeling in data science typically requires unknown parameters that can be inferred from observational data. A parameter is any numerical quantity that characterizes a given data set or some aspect of it. This study focuses on the effectiveness of combining data sets from different predictive models that share some common parameters. We will show preliminary results demonstrating the effect of uncertainties in initial parameter estimates inferred from a secondary data set.

AB - Predictive modeling in data science typically requires unknown parameters that can be inferred from observational data. A parameter is any numerical quantity that characterizes a given data set or some aspect of it. This study focuses on the effectiveness of combining data sets from different predictive models that share some common parameters. We will show preliminary results demonstrating the effect of uncertainties in initial parameter estimates inferred from a secondary data set.

UR - https://scholarworks.boisestate.edu/gss_2019/93

M3 - Presentation

ER -

Predictive Models in Data Science

Abstract

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