HCAD 750 Module 5 study guide questions
with correct answers
data |mining |- |VERIFIED |ANSWER✔✔-the |process |of |finding |correlations |or |patterns |among |the |data
- |facilitates |data |exploration
- |extract |useful |knowledge |hidden |in |data
HIPPA |in |relation |to |Data |mining |- |VERIFIED |ANSWER✔✔-using |patient |data |for |any |purpose |beyond |
providing |care |for |the |individual |patient |brings |with |it |some |tricky |issues |regarding |privacy, |and |
keeping |the |information |from |falling |into |the |wrong |hands. |There |are |significant |legal |issues |related |
to |the |use |of |patient |data |in |data |mining |efforts, |specifically |related |to |the |de-identification, |
aggregation, |and |storage |of |the |data. |Failing |to |take |the |appropriate |steps |when |using |personal |
health |data |as |a |tool |for |population |health |could |lead |to |serious |consequences
Predictive |Data |Mining |- |VERIFIED |ANSWER✔✔--perform |induction |on |the |current |data |in |order |to |
make |predictions.
Meta-learning |- |VERIFIED |ANSWER✔✔--ability |for |a |device, |machine, |etc. |to |be |able |to |take |in |
numerous |types |of |data |and |learn |from |the |data |in |order |to |produce |knowledge.
Machine |Learning |- |VERIFIED |ANSWER✔✔-- |investigates |how |computers |can |learn |based |on |data
- |automatically |learn |to |recognize |complex |patterns |and |make |intelligent |decisions |on |their |own |
based |on |the |data
Feature |Selection |- |VERIFIED |ANSWER✔✔-refers |to |the |process |of |reducing |the |inputs |for |processing |
and |analysis, |or |finding |the |most |meaningful |inputs.
Data |reduction |- |VERIFIED |ANSWER✔✔-- |be |applied |to |obtain |a |compressed |representation |of |the |
data |set |that |is |much |smaller |in |volume, |yet |maintains |the |integrity |of |the |original |data.
- |used |when |the |data |selected |is |too |complex |or |huge
, drill |down |- |VERIFIED |ANSWER✔✔-to |request |or |seek |out |additional |information |on |a |specific |subject.
|Makes |the |data |more |detailed.
Stacking |- |VERIFIED |ANSWER✔✔-- |is |an |ensemble |of |models |combined |sequentially. |
- |can |be |used |to |classify |data
- |get |a |meta-learning |device, |stack |the |data |in |the |device, |the |base |learner |is |combined |and |produces
|the |data |information |needed.
Boosting |- |VERIFIED |ANSWER✔✔-- |each |of |the |data |classifications |are |weighted. |
- |once |the |system |learns, |it |is |able |to |continuously |update |and |learn |which |ones |are |incorrect, |and |
the |weight |shifts |to |reflect |the |accuracy
Bagging |(Bootstrap |Aggregating) |- |VERIFIED |ANSWER✔✔-- |method |used |to |increase |accuracy |with |
data |mining
- |majority |vote; |more |times |a |classification |is |picked, |the |more |reliable |the |data. |
- |algorithm |creates |an |ensemble |of |models |for |learning |scheme |where |each |model |gives |an |equally |
weighted |prediction
Six |Sigma |- |VERIFIED |ANSWER✔✔-DMAIC |steps: |define, |measure, |analyze, |improve, |and |control
- |can |explain |why |data |behaves |a |certain |way
- |not |necessarily |a |data |mining |technique, |but |a |model |used |to |give |more |of |answer |to |"why" |and
|"how" |in |regard |to |data |information.
- |adds |additional |steps |to |mining |that |yields |better |results
Big |Data |- |VERIFIED |ANSWER✔✔-is |a |term |that |describes |the |large |volume |of |data |- |both |structured |
and |unstructured |- |that |inundates |a |business |on |a |day-to-day |basis. |But |it's |not |the |amount |of |data |
that's |important. |It's |what |organizations |do |with |the |data |that |matters. |Big |data |can |be |analyzed |for |
insights |that |lead |to |better |decisions |and |strategic |business |moves.
Exploratory |data |analysis |(EDA) |- |VERIFIED |ANSWER✔✔-how |we |make |sense |of |the |data |by |
converting |them |from |their |raw |form |to |a |more |informative |one
- |sometimes |known |as |model |building |or |pattern |id
with correct answers
data |mining |- |VERIFIED |ANSWER✔✔-the |process |of |finding |correlations |or |patterns |among |the |data
- |facilitates |data |exploration
- |extract |useful |knowledge |hidden |in |data
HIPPA |in |relation |to |Data |mining |- |VERIFIED |ANSWER✔✔-using |patient |data |for |any |purpose |beyond |
providing |care |for |the |individual |patient |brings |with |it |some |tricky |issues |regarding |privacy, |and |
keeping |the |information |from |falling |into |the |wrong |hands. |There |are |significant |legal |issues |related |
to |the |use |of |patient |data |in |data |mining |efforts, |specifically |related |to |the |de-identification, |
aggregation, |and |storage |of |the |data. |Failing |to |take |the |appropriate |steps |when |using |personal |
health |data |as |a |tool |for |population |health |could |lead |to |serious |consequences
Predictive |Data |Mining |- |VERIFIED |ANSWER✔✔--perform |induction |on |the |current |data |in |order |to |
make |predictions.
Meta-learning |- |VERIFIED |ANSWER✔✔--ability |for |a |device, |machine, |etc. |to |be |able |to |take |in |
numerous |types |of |data |and |learn |from |the |data |in |order |to |produce |knowledge.
Machine |Learning |- |VERIFIED |ANSWER✔✔-- |investigates |how |computers |can |learn |based |on |data
- |automatically |learn |to |recognize |complex |patterns |and |make |intelligent |decisions |on |their |own |
based |on |the |data
Feature |Selection |- |VERIFIED |ANSWER✔✔-refers |to |the |process |of |reducing |the |inputs |for |processing |
and |analysis, |or |finding |the |most |meaningful |inputs.
Data |reduction |- |VERIFIED |ANSWER✔✔-- |be |applied |to |obtain |a |compressed |representation |of |the |
data |set |that |is |much |smaller |in |volume, |yet |maintains |the |integrity |of |the |original |data.
- |used |when |the |data |selected |is |too |complex |or |huge
, drill |down |- |VERIFIED |ANSWER✔✔-to |request |or |seek |out |additional |information |on |a |specific |subject.
|Makes |the |data |more |detailed.
Stacking |- |VERIFIED |ANSWER✔✔-- |is |an |ensemble |of |models |combined |sequentially. |
- |can |be |used |to |classify |data
- |get |a |meta-learning |device, |stack |the |data |in |the |device, |the |base |learner |is |combined |and |produces
|the |data |information |needed.
Boosting |- |VERIFIED |ANSWER✔✔-- |each |of |the |data |classifications |are |weighted. |
- |once |the |system |learns, |it |is |able |to |continuously |update |and |learn |which |ones |are |incorrect, |and |
the |weight |shifts |to |reflect |the |accuracy
Bagging |(Bootstrap |Aggregating) |- |VERIFIED |ANSWER✔✔-- |method |used |to |increase |accuracy |with |
data |mining
- |majority |vote; |more |times |a |classification |is |picked, |the |more |reliable |the |data. |
- |algorithm |creates |an |ensemble |of |models |for |learning |scheme |where |each |model |gives |an |equally |
weighted |prediction
Six |Sigma |- |VERIFIED |ANSWER✔✔-DMAIC |steps: |define, |measure, |analyze, |improve, |and |control
- |can |explain |why |data |behaves |a |certain |way
- |not |necessarily |a |data |mining |technique, |but |a |model |used |to |give |more |of |answer |to |"why" |and
|"how" |in |regard |to |data |information.
- |adds |additional |steps |to |mining |that |yields |better |results
Big |Data |- |VERIFIED |ANSWER✔✔-is |a |term |that |describes |the |large |volume |of |data |- |both |structured |
and |unstructured |- |that |inundates |a |business |on |a |day-to-day |basis. |But |it's |not |the |amount |of |data |
that's |important. |It's |what |organizations |do |with |the |data |that |matters. |Big |data |can |be |analyzed |for |
insights |that |lead |to |better |decisions |and |strategic |business |moves.
Exploratory |data |analysis |(EDA) |- |VERIFIED |ANSWER✔✔-how |we |make |sense |of |the |data |by |
converting |them |from |their |raw |form |to |a |more |informative |one
- |sometimes |known |as |model |building |or |pattern |id
