Embed Size (px)
Citation preview
Introduction to Machine Learning
Classification: Tasks
compstat-lmu.github.io/lecture_i2ml
CLASSIFICATION
Learn functions that assign class labels to observation / feature vectors.Each observation belongs to exactly one class. The main difference toregression is the scale of the output / label.
SepalLength
SepalWidth
PetalLength
PetalWidth
Species
5.1 3.5 1.4 0.2 setosa
5.9 3.0 5.1 1.8 virginica
Classifier
SepalLength
SepalWidth
PetalLength
PetalWidth
Species
5.4 3.3 3.2 1.1 ???
New Data withunknown label
New Class label
Our Data
c© Introduction to Machine Learning – 1 / 8
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 0.05V1
V2
Class
● M
R
Sonar
●●● ●●
●
●
●●
●
●●
●●
●
●●
● ●●
●
●
●
●
●●
●
●● ●●
●
●
●●●●
●
● ●
●●
●
●
●
●
●●●●
0.0
0.5
1.0
1.5
2.0
2.5
2 4 6Petal.Length
Pet
al.W
idth Species
● setosa
versicolor
virginica
Iris
c© Introduction to Machine Learning – 2 / 8
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
c© Introduction to Machine Learning – 3 / 8
BINARY CLASSIFICATION TASK - EXAMPLEShttps://www.bendbulletin.com/localstate/deschutescounty/3430324-151/fact-or-fiction-polygraphs-just-an-investigative-tool
c© Introduction to Machine Learning – 4 / 8
MULTICLASS TASK - MEDICAL DIAGNOSIS
https://symptoms.webmd.com
c© Introduction to Machine Learning – 5 / 8
MULTICLASS TASK - IRIS
The iris dataset was introduced by the statistician Ronald Fisher and isone of the most frequent used datasets. Originally it was designed forlinear discriminant analysis.
Setosa Versicolor Virginica
Source:https://en.wikipedia.org/wiki/Iris_flower_data_set
c© Introduction to Machine Learning – 6 / 8
MULTICLASS TASK - IRIS
150 iris flowers
Predict subspecies
Based on sepal and petallength / 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
c© Introduction to Machine Learning – 7 / 8
MULTICLASS TASK - IRIS
●
●●
●
●
●
● ●
●●
●
●
●●
●
●
●
●
●●
●
●●
●●
●
●●●
●●
●
●●
●●
●●
●
●●
●
●
●
●
●
●
●
●
●●●
●
●
●●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●
●
●●
●●
●●
●●●
● ●
●
●
●
●
●
●●
●
●
●
●
●● ●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
● ●●
●●
●●
●●
●
●
●●●
●
●
●●
●● ●
●
●●
●
●
●
●
●
●●●● ●●
● ●● ● ●●●● ●
●●●●● ●●
●
●●●● ●●●● ●● ●●● ●●● ●●●●●●
● ●● ●●
●●●
●
●● ●
●
●
●●
●●
●
●
●●●
●
●
●
●
●●● ●
●●●
●●● ●
●
● ● ●●
●●● ●
●
●
●●● ●
●
●
●
●
●● ●
●
●
●●
●
●● ●●● ●●
●●
●
●
●
●
●
●●
●●
● ●●
●
●●
●●
●●
●
●●●●
●●●● ●●
●
●●●● ●●
●●●
●●●
●●●
●●● ●●
●●
●
●
● ●●
●●●●●
●●●● ●
●● ●
●●●
●●
●●● ●●
●● ●
●●
●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●●
●●
● ●
●●
●●●
●
●●
●●
●●●●
●●
●
●●● ●
●●
●
●●
●
●●
●●●
●
●●
●●
●●
●
●●
●
●
● ●●
●
●●●
●
●
●●
●
●●
●●
●●
●
●●
●
●●●
●●
●
●
Cor : −0.118setosa: 0.743
versicolor: 0.526virginica: 0.457
●● ●● ●●
●●● ● ●●●● ●
●●●●●● ●
●
●●● ●●●●● ● ●●●
● ●●● ●●● ●●
●● ●● ●●
●●●
●
●● ●
●
●
●●
●●
●
●
●●●
●
●
●
●
● ●●●
● ●●
●●● ●
●
● ●●●
●●● ●
●
●
● ●●●
●
●
●
●
●●●
●
●
●●
●
●● ●● ● ●●
●●
●
●
●
●
●
●●
● ●
● ●●
●
●●
●●
●●
●
●●●●
●●●● ● ●●
●● ●● ●●
●●●
●●●
●●●
●●● ●●
●●
●
●
●●●
●●●●●
●●●● ●
●● ●
●●●
●●
●●● ●●
●●●
●●●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●●
●●
●●
●●
●●●
●
●●
●●
● ●●●
●●
●
●●
●●●
●
●
●●
●
●●
●●●
●
●●
●●
●●
●
●●
●
●
●●●
●
●● ●
●
●
●●
●
●●
●●
●●
●
●●
●
●●
●
●●
●
●
Cor : 0.872setosa: 0.267
versicolor: 0.754virginica: 0.864
Cor : −0.428setosa: 0.178
versicolor: 0.561virginica: 0.401
●●●●●●
●●●●●●
●●●
●●●●●
●●
●
●
●●●
●●●●●
●●●●●●
●●●●●
●●
●●●●●
●● ●
●●
●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●●
●●● ●
●●
●●
●●
●●●
●●●●●
●●
●
●●●●
●●
●
●●
●
●●
●●●
●
●●
●●
●●
●
●●
●
●
● ●●
●
●●●
●
●
●●
●
●●
●●
●●
●
●●
●
●●
●
●●
●
●
Cor : 0.818setosa: 0.278
versicolor: 0.546virginica: 0.281
Cor : −0.366setosa: 0.233
versicolor: 0.664virginica: 0.538
Cor : 0.963setosa: 0.332
versicolor: 0.787virginica: 0.322
●
●
●
●
●
●
●●
●●
●
●●
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Sepal.Length
Sepal.W
idthP
etal.LengthP
etal.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 setosaversicolorvirginica
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
0.02.55.07.5
0.02.55.07.5
0.02.55.07.5
c© Introduction to Machine Learning – 8 / 8
