Suppose we have the following decision tree to classify the iris data. Now we have a new sample with sepal length = 6cm, sepal width = 3cm, petal length = 5.5 cm, petal width = 1 cm. What is the predicted class of this sample? petal width (cm) <= 0.8 gini = 0.665 samples = 90 value = [27, 31, 32] class = virginica False True gini = 0.0 samples = 27 value = [27, 0, 0] class = setosa petal width (cm) <= 1.75 gini = 0.5 samples = 63 value = [0, 31, 32] class = virginica petal length (cm) <= 5.35 gini = 0.208 samples = 34 value = [0, 30, 4] class = versicolor petal length (cm) <= 4.85 gini = 0.067 samples = 29 value = [0, 1, 28] class = virginica sepal length (cm) <= 4.95 gini = 0.117 samples = 32 value = [0, 30, 2] class = versicolor gini = 0.0 samples = 2 value = [0, 0, 2] class = virginica sepal length (cm) <= 5.95 gini = 0.444 samples = 3 value = [0, 1, 2] class = virginica gini = 0.0 samples = 26 value = [0, 0, 26] class = virginica petal length (cm) <= 3.9 gini = 0.5 samples = 2 value = [0, 1, 1] class = versicolor sepal width (cm) <= 2.25 gini = 0.064 samples = 30 value = [0, 29, 1] class = versicolor gini = 0.0 samples = 1 value = [0, 1, 0] class = versicolor gini = 0.0 samples = 2 value = [0, 0, 2] class = virginica gini = 0.0 samples = 1 value = [0, 1, 0] class = versicolor gini = 0.0 samples = 1 value = [0, 0, 1] class = virginica gini = 0.5 samples = 2 value = [0, 1, 1] class = versicolor gini = 0.0 samples = 28 value = [0, 28, 0] class = versicolor versicolor virginica setosa
Suppose we have the following decision tree to classify the iris data. Now we have a new sample with sepal length = 6cm, sepal width = 3cm, petal length = 5.5 cm, petal width = 1 cm. What is the predicted class of this sample? petal width (cm) <= 0.8 gini = 0.665 samples = 90 value = [27, 31, 32] class = virginica False True gini = 0.0 samples = 27 value = [27, 0, 0] class = setosa petal width (cm) <= 1.75 gini = 0.5 samples = 63 value = [0, 31, 32] class = virginica petal length (cm) <= 5.35 gini = 0.208 samples = 34 value = [0, 30, 4] class = versicolor petal length (cm) <= 4.85 gini = 0.067 samples = 29 value = [0, 1, 28] class = virginica sepal length (cm) <= 4.95 gini = 0.117 samples = 32 value = [0, 30, 2] class = versicolor gini = 0.0 samples = 2 value = [0, 0, 2] class = virginica sepal length (cm) <= 5.95 gini = 0.444 samples = 3 value = [0, 1, 2] class = virginica gini = 0.0 samples = 26 value = [0, 0, 26] class = virginica petal length (cm) <= 3.9 gini = 0.5 samples = 2 value = [0, 1, 1] class = versicolor sepal width (cm) <= 2.25 gini = 0.064 samples = 30 value = [0, 29, 1] class = versicolor gini = 0.0 samples = 1 value = [0, 1, 0] class = versicolor gini = 0.0 samples = 2 value = [0, 0, 2] class = virginica gini = 0.0 samples = 1 value = [0, 1, 0] class = versicolor gini = 0.0 samples = 1 value = [0, 0, 1] class = virginica gini = 0.5 samples = 2 value = [0, 1, 1] class = versicolor gini = 0.0 samples = 28 value = [0, 28, 0] class = versicolor versicolor virginica setosa
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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![Suppose we have the following decision tree to classify the iris data. Now we have a
new sample with sepal length = 6cm, sepal width = 3cm, petal length = 5.5 cm, petal
width = 1 cm. What is the predicted class of this sample?
petal width (cm) <= 0.8
gini = 0.665
samples = 90
value = [27, 31, 32]
class = virginica
False
True
gini = 0.0
samples = 27
value = [27, 0, 0]
class = setosa
petal width (cm) <= 1.75
gini = 0.5
samples = 63
value = [0, 31, 32]
class = virginica
petal length (cm) <= 5.35
gini = 0.208
samples = 34
value = [0, 30, 4]
class = versicolor
petal length (cm) <= 4.85
gini = 0.067
samples = 29
value = [0, 1, 28]
class = virginica
sepal length (cm) <= 4.95
gini = 0.117
samples = 32
value = [0, 30, 2]
class = versicolor
gini = 0.0
samples = 2
value = [0, 0, 2]
class = virginica
sepal length (cm) <= 5.95
gini = 0.444
samples = 3
value = [0, 1, 2]
class = virginica
gini = 0.0
samples = 26
value = [0, 0, 26]
class = virginica
petal length (cm) <= 3.9
gini = 0.5
samples = 2
value = [0, 1, 1]
class = versicolor
sepal width (cm) <= 2.25
gini = 0.064
samples = 30
value = [0, 29, 1]
class = versicolor
gini = 0.0
samples = 1
value = [0, 1, 0]
class = versicolor
gini = 0.0
samples = 2
value = [0, 0, 2]
class = virginica
gini = 0.0
samples = 1
value = [0, 1, 0]
class = versicolor
gini = 0.0
samples = 1
value = [0, 0, 1]
class = virginica
gini = 0.5
samples = 2
value = [0, 1, 1]
class = versicolor
gini = 0.0
samples = 28
value = [0, 28, 0]
class = versicolor
versicolor
virginica
setosa](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fe5f558a7-14fc-4024-84d6-4debb1adc6f6%2F4b697587-bbea-4280-af8a-957e1512cc1b%2Fpqazlnm_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Suppose we have the following decision tree to classify the iris data. Now we have a
new sample with sepal length = 6cm, sepal width = 3cm, petal length = 5.5 cm, petal
width = 1 cm. What is the predicted class of this sample?
petal width (cm) <= 0.8
gini = 0.665
samples = 90
value = [27, 31, 32]
class = virginica
False
True
gini = 0.0
samples = 27
value = [27, 0, 0]
class = setosa
petal width (cm) <= 1.75
gini = 0.5
samples = 63
value = [0, 31, 32]
class = virginica
petal length (cm) <= 5.35
gini = 0.208
samples = 34
value = [0, 30, 4]
class = versicolor
petal length (cm) <= 4.85
gini = 0.067
samples = 29
value = [0, 1, 28]
class = virginica
sepal length (cm) <= 4.95
gini = 0.117
samples = 32
value = [0, 30, 2]
class = versicolor
gini = 0.0
samples = 2
value = [0, 0, 2]
class = virginica
sepal length (cm) <= 5.95
gini = 0.444
samples = 3
value = [0, 1, 2]
class = virginica
gini = 0.0
samples = 26
value = [0, 0, 26]
class = virginica
petal length (cm) <= 3.9
gini = 0.5
samples = 2
value = [0, 1, 1]
class = versicolor
sepal width (cm) <= 2.25
gini = 0.064
samples = 30
value = [0, 29, 1]
class = versicolor
gini = 0.0
samples = 1
value = [0, 1, 0]
class = versicolor
gini = 0.0
samples = 2
value = [0, 0, 2]
class = virginica
gini = 0.0
samples = 1
value = [0, 1, 0]
class = versicolor
gini = 0.0
samples = 1
value = [0, 0, 1]
class = virginica
gini = 0.5
samples = 2
value = [0, 1, 1]
class = versicolor
gini = 0.0
samples = 28
value = [0, 28, 0]
class = versicolor
versicolor
virginica
setosa
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