Match each the following example datasets (X,y) on the left to the most logical type of supervised learning model to use on that dataset. Here, X is the input/predictors/features of the data while y is the output/target: X = (moms height in centimeters, dad's [ Choose ] height in centimeters); y = (child's height in centimeters) [ Choose ] multivariate linear regression X = (age in years, income in USD, years of experience); y = (hired? true/false) multiclass logistic regression binary logistic regression X = (number of years experience driving); y = (number of car accidents %3D linear classification %3D involved in -- Hint: this can be logistic polynomial regression assumed as continuous in this problem due to large amount) univariate linear regression multiclass polynomial regression X = (age, income, level of with various political positions); y = agreement TChoose (Which of 4 candidates for whom they plan to vote)

Match each the following example datasets (X,y) on the left to the most logical type of supervised learning model to use on that dataset. Here, X is the input/predictors/features of the data while y is the output/target: X = (moms height in centimeters, dad's [ Choose ] height in centimeters); y = (child's height in centimeters) [ Choose ] multivariate linear regression X = (age in years, income in USD, years of experience); y = (hired? true/false) multiclass logistic regression binary logistic regression X = (number of years experience driving); y = (number of car accidents %3D linear classification %3D involved in -- Hint: this can be logistic polynomial regression assumed as continuous in this problem due to large amount) univariate linear regression multiclass polynomial regression X = (age, income, level of with various political positions); y = agreement TChoose (Which of 4 candidates for whom they plan to vote)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Concept explainers

System Model Approaches

The systems model approach is a process-oriented representation that emphasizes the impact or flow of information between modules. The multidisciplinary study of the use of models to design and create systems in business and IT development is known as systems model approach. It models how processes interact and what these processes do, but it does not go into detail about how these processes are implemented. Functional decomposition may be utilized to enhance the models employed in this technique, and they can be coupled to requirements models for additional system partitioning.

Question

100%

Match each the following example datasets (X,y) on the left to the most logical type of
supervised learning model to use on that dataset. Here, X is the
input/predictors/features of the data while y is the output/target:
X = (moms height in centimeters, dad's
(child's
[ Choose ]
height in centimeters); y =
height in centimeters)
[ Choose ]
multivariate linear regression
X = (age in years, income in USD, years
of experience); y = (hired? true/false)
multiclass logistic regression
%3D
binary logistic regression
X = (number of years experience
driving); y = (number of car accidents
linear classification
%3D
involved in -- Hint: this can be
logistic polynomial regression
assumed as continuous in this problem
due to large amount)
univariate linear regression
multiclass polynomial regression
X = (age, income, level of agreement
with various political positions); y =
%3D
T Choose ]
(Which of 4 candidates for whom they
plan to vote)

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