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Flashcards in Introduction Data types and Learning Types Deck (11)
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1
Q

Structured data

A

Numerical data
(age, time, temperature)

Categorical data
(gender, color, country,
class)
2
Q

Unstructured data

A
  • Text
  • Audio
  • Image
  • Signal
  • Video
3
Q

Structured data (tree)

A
4
Q

Tabular data

A

Columns are features and rows are instances

5
Q

Features

A

• Features are raw or derived: max, min, average, rank,
bin, etc.

• Time plays a special role: time cannot decrease and
often we want to predict the future based on the
past.

• In case of labeled data, there are descriptive features
and a target feature.

6
Q

Labeled tabular data

A

Descriptive features

Target feature

7
Q

• Alternative names for descriptive features

A
  • predictor variables
  • independent variables
8
Q

• Alternative names for target feature

A
  • response variable
  • dependent variable
9
Q

• Alternative names for instances

A
  • individuals, entities,
    cases, objects, or records.
10
Q

Supervised learning using labeled data (goal)

A

The goal is to find a “rule”

in terms descriptive
features that explains the
target feature as good as
possible.

11
Q

Unsupervised learning (goal)

A

The goal is to find clusters
or patterns.

• Clusters are
homogeneous sets of
instances.

• Patterns reveal hidden
structures in the data (unknown unknowns)