Introduction to Machine Learning Classification: Tasks Sonar - - PowerPoint PPT Presentation

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Sep 24, 2023 426 likes •513 views

Introduction to Machine Learning Classification: Tasks Sonar Learning goals 0.20 Understand the main difference between regression and 0.15 classification Response V2 M R 0.10 Know that classification can be binary or multiclass 0.05

SLIDE 1

Introduction to Machine Learning Classification: Tasks

0.00 0.05 0.10 0.15 0.20 0.00 0.01 0.02 0.03 0.04 0.05

V1 V2 Response

M R

Sonar

Learning goals

Understand the main difference between regression and classification Know that classification can be binary or multiclass Know some examples of classification tasks

SLIDE 2

CLASSIFICATION

Learn functions that assign class labels to observation / feature vectors. Each observation belongs to exactly one class. The main difference to regression is the scale of the output / label.

Sepal Length Sepal Width Petal Length Petal Width Species 5.1 3.5 1.4 0.2 setosa 5.9 3.0 5.1 1.8 virginica

Classifier

Sepal Length Sepal Width Petal Length Petal Width Species 5.4 3.3 3.2 1.1

???

New Data with unknown label New Class label Our Data c

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SLIDE 3

BINARY AND MULTICLASS TASKS

The task can contain 2 classes (binary) or multiple (multiclass).

0.00 0.05 0.10 0.15 0.20 0.00 0.01 0.02 0.03 0.04 0.05

V1 V2 Response

M R

Sonar

0.0 0.5 1.0 1.5 2.0 2.5 2 4 6

Petal.Length Petal.Width Response

setosa versicolor virginica

Iris

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SLIDE 4

BINARY CLASSIFICATION TASK - EXAMPLES

Credit risk prediction, based on personal data and transactions Spam detection, based on textual features Churn prediction, based on customer behavior Predisposition for specific illness, based on genetic data

https://www.bendbulletin.com/localstate/deschutescounty/3430324-151/fact-or-fiction-polygraphs-just-an-investigative-tool c

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SLIDE 5

MULTICLASS TASK - MEDICAL DIAGNOSIS

https://symptoms.webmd.com c

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SLIDE 6

MULTICLASS TASK - IRIS

The iris dataset was introduced by the statistician Ronald Fisher and is

ne of the most frequent used data sets. Originally, it was designed for

linear discriminant analysis.

Setosa Versicolor Virginica

Source:

https://en.wikipedia.org/wiki/Iris_flower_data_set

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SLIDE 7

MULTICLASS TASK - IRIS

150 iris flowers Predict subspecies Based on sepal and petal length / width in [cm]

## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1: 5.1 3.5 1.4 0.2 setosa ## 2: 4.9 3.0 1.4 0.2 setosa ## 3: 4.7 3.2 1.3 0.2 setosa ## 4: 4.6 3.1 1.5 0.2 setosa ## 5: 5.0 3.6 1.4 0.2 setosa ##

## 146:

6.7 3.0 5.2 2.3 virginica ## 147: 6.3 2.5 5.0 1.9 virginica ## 148: 6.5 3.0 5.2 2.0 virginica ## 149: 6.2 3.4 5.4 2.3 virginica ## 150: 5.9 3.0 5.1 1.8 virginica

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SLIDE 8

MULTICLASS TASK - IRIS

Corr: −0.118 setosa: 0.743*** versicolor: 0.526*** virginica: 0.457*** Corr: 0.872*** setosa: 0.267. versicolor: 0.754*** virginica: 0.864*** Corr: −0.428*** setosa: 0.178 versicolor: 0.561*** virginica: 0.401** Corr: 0.818*** setosa: 0.278. versicolor: 0.546*** virginica: 0.281* Corr: −0.366*** setosa: 0.233 versicolor: 0.664*** virginica: 0.538*** Corr: 0.963*** setosa: 0.332* versicolor: 0.787*** virginica: 0.322*

Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length Sepal.Width Petal.Length Petal.Width Species 5 6 7 8 2.0 2.5 3.0 3.5 4.0 4.5 2 4 6 0.0 0.5 1.0 1.5 2.0 2.5 setosaversicolor virginica 0.0 0.4 0.8 1.2 2.0 2.5 3.0 3.5 4.0 4.5 2 4 6 0.0 0.5 1.0 1.5 2.0 2.5 0.0 2.5 5.0 7.5 0.0 2.5 5.0 7.5 0.0 2.5 5.0 7.5

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