Qarorlar daraxtining amaliy tadbiqlari
![Entropy(2/3, 1/3)=-2/3*log2(2/3)-1/3*log2(1/3)=0.918
Entropy(2/3, 1/3)= -2/3*log2(2/3)-1/3*log2(1/3)=0.918
Info(“ C “)=Info ([2,2], [2,1] ,[2,1])=1*4/10+0.918*3/10+0.918*3/10 =0.9508
InfoGain(“C”)=IBS-Info(“C”)=0.971-0.9508=0.0202
Misol-2 Quyidagi data setdan foydalangan holda decision tree (Gini Index)
algoritmi bo’yicha berilgan masalani barcha nominal atributlar ( Outlook ,
Temperature, Humidity, Windy) uchun yeching.
N% Outlook Temperatur
e Humidity Windy Class
1 Sunny Hot High False No
2 Sunny Hot High True No
3 Overcast Hot High False Yes
4 Rainy Mild High False Yes
5 Rainy Cool Normal False Yes
6 Rainy Cool Normal True No
7 Overcast Cool Normal True Yes
8 Sunny Mild High False No
9 Sunny Cool Normal False Yes
10 Rainy Mild Normal False Yes
11 Sunny Mild Normal True Yes
12 Overcast Mild High True Yes
13 Overcast Hot Normal False Yes
14 Rainy Mild High True No
Outlook:
C (ustun
nomi) Class
(Yes) Class
(No) P(Yes) P(No) Entropy Probability
Sunny 2 3 2/5 3/5 0.97 5/14
Overcast 4 0 1 0 0 4/14
Rainy 3 2 3/5 2/5 0.97 5/14
Classlar soni bo’yicha IBS hisonlanadi . Yes- 9, No- 5.
IBS=Entropy(5,9)= -5/14*log2(5/14)-9/14*log2(9/14)=0.94
Entropy(1,0)=0
Entropy(2/5, 3/5)=-2/5*log2(2/5)-3/5*log2(3/5)=0.97
Entropy(3/5, 2/5)= -3/5*log2(3/5)-2/5*log2(2/5)=0.97
25](https://docx.uz/documents/037ca349-71ea-4d1f-bc7a-c6e4f186e7c1/page_24.png?v=1)
![Info(“ outlok “)=Info ([2,3], [4,0] ,[3,2)=0.97*5/14+0.97*5/14 =0.69
InfoGain(“outlok”)=IBS-Info(“C”)=0.971-0.69=0.25
import pandas
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
df = pandas.read_csv("datasets/golf_df.csv")
df.info
d = {'sunny': 0, 'overcast': 1,'rainy':2}
df['Outlook'] = df['Outlook'].map(d)
d = {'hot': -1, 'mild': 0,'cool':1}
df['Temperature'] = df['Temperature'].map(d)
d = {'high': 1, 'normal': 0}
df['Humidity'] = df['Humidity'].map(d)
d = {True: 1, False: 0}
df['Windy'] = df['Windy'].map(d)
d = {'no': 0, 'yes': 1}
df['Play'] = df['Play'].map(d)
print(df)
attributes = ['Outlook', 'Temperature', 'Humidity', 'Windy']
X = df[attributes]
y = df['Play']
dtree = DecisionTreeClassifier()
dtree = dtree.fit(X, y)
print(dtree.predict([[0,0,0,0]]))
tree.plot_tree(dtree, feature_names=attributes)
plt.show()
26](https://docx.uz/documents/037ca349-71ea-4d1f-bc7a-c6e4f186e7c1/page_25.png?v=1)
![Ushbu dastur, Python tilida scikit-learn kutubxonasi (library)
yordamida ma'lumotlarni o'rganish uchun ishlatiladi. Bu dastur
DecisionTreeClassifier orqali qaror qabul qilishning
moslashtirilgan algoritmi, ya'ni qaror daraxtini (decision tree) yaratadi.
Kodda quyidagi ishlar amalga oshirilgan:
1. Ma'lumotlarni Yuklash: pandas kutubxonasi orqali CSV formatidagi
ma'lumotlar yuklangan va DataFrame (df) ko'rinishida saqlangan.
2. Ma'lumotlar Bilan Ishlash: DataFrame'dagi kategorik qiymatlarni
raqamli qiymatlarga aylantirish uchun map metodidan foydalanilgan.
Misol uchun, 'Outlook' stoladagi 'sunny', 'overcast', 'rainy' qiymatlarini
raqamli qiymatlar bilan almashtirilgan.
3. Modelni O'rganish: DecisionTreeClassifier obyekti yaratilgan va
ma'lumotlar uchun xususiyatlar ( X ) va niyatlar ( y ) ajratilgan. Keyinchalik,
fit metodi yordamida model o'rgatilgan.
4. Modeldan Foydalanish: dtree.predict orqali berilgan ma'lumotlar
uchun modelning baho berishini hisoblangan. Bu kodda [0,0,0,0]
qiymatlar uchun "Play" qilish (golf o'ynash) ehtimoliyatini aniqlash uchun
so'ralgan.
5. Daraxtni Ko'rish: tree.plot_tree yordamida daraxtni (decision
tree) vizualizatsiya qilishga harakat qilingan. Bu esa modelning qaror
qabul qilish jarayonini o'qishga yordam bergan.
27](https://docx.uz/documents/037ca349-71ea-4d1f-bc7a-c6e4f186e7c1/page_26.png?v=1)
![FOYDALANILGAN ADABIYOTLAR
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3. Kelley, Henry J. (1960). "Gradient theory of optimal flight paths".
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the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961
5. Werbos, Paul (1982). "Applications of advances in nonlinear
sensitivity analysis" (PDF). System modeling and optimization. Springer. pp. 762–
770. Archived (PDF) from the original on 14 April 2016. Retrieved 2 July 2017.
6. Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams.
"Learning Internal Representations by Error Propagation". David E. Rumelhart,
James L. McClelland, and the PDP research group. (editors), Parallel distributed
processing: Explorations in the microstructure of cognition, Volume 1:
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7. Hastie, Trevor. Tibshirani, Robert. Friedman, Jerome. The Elements
of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New
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8. Rosenblatt, Frank (1958). "The Perceptron: A Probabilistic Model For
Information Storage And Organization in the Brain". Psychological Review. 65
(6): 386–408. CiteSeerX 10.1.1.588.3775. doi:10.1037/h0042519. PMID
13602029. S2CID 12781225.
9. Rosenblatt, Frank (1962). Principles of Neurodynamics. Spartan, New
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10. Schmidhuber, Juergen (2022). "Annotated History of Modern AI and
Deep Learning". arXiv:2212.11279 [cs.NE].
30](https://docx.uz/documents/037ca349-71ea-4d1f-bc7a-c6e4f186e7c1/page_29.png?v=1)
QARORLAR DARAXTINING AMALIY TADBIQLARI