Classification

Computing and Data Science
Classification is the problem of categorizing an input from among two or more populations.

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⟶
Spam
Classifier
⟶
"Spam"

Bank Transaction —
$1500 worth of Doritos
⟶
Fraud
Classifier
⟶
"Fraud"

⟶
Image
Classifier
⟶
"Cat"

Give an example of a height that:

  1. Is almost definitely a middle school student.
  2. Is almost definitely a high school student.
  3. Could be from either population with roughly equal probability.


School_Level StudentID Height_cm
Middle School 103154 149
Middle School 102917 161
High School 100662 165
High School 100341 177
Middle School 103509 155
High School 101802 172
Middle School 103193 159
High School 102286 160
High School 100775 181
Middle School 103564 152
  • Output
  • Dependent Variable
  • Response Variable
  • Label
  • Target
  • Class
  • Input
  • Independent Variables
  • Explanatory Variable
  • Attributes
  • Predictors
  • Features

A Classification Problem

We are given an image and want to classify it as "Apple", "Orange", "Banana", or "Blueberry"

A Classification Problem

We are given an image and want to classify it as "Apple", "Orange", "Banana", or "Blueberry"

⟶
Classifier
 ⟶ "Apple"

Feature Engineering a Fruit Classifier

Gather all of the "Apple" images. For each image, add up the total R values, G values, and B values.

Feature Engineering a Fruit Classifier

Gather all of the "Apple" images. For each image, add up the total R values, G values, and B values.

Feature Engineering a Fruit Classifier


Feature Engineering a Fruit Classifier



RGB sum = (2.15, 2.21, 1.8) Million ⟶ ?

Feature Engineering a Fruit Classifier

Given an input image, our Fruit Classifier Algorithm is:
  1. Add up all of the R, G, and B values
  2. If ... then ...
  3. Else if ... then ...
  4. Else if ... then ...
  5. Else ...

Some of the well known harms

©2025 Jedediyah Williams
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Attribution-NonCommercial-ShareAlike 4.0 International License.

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