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katta. Nutqni tanib olish bugungi kunda eng muhim dasturlardan biridir. Taniqli bosma
yoki qo‘lda yozilgan OCR yozib olinishi va nutq chiqishi mumkin edi. Bu ko‘zi
ojizlarga ma’lumot yuborish va qabul qilishga yordam beradi.
Foydalanilgan adabiyotlar roʻyxati:
1.
Choryorqulov G‘.H., & Qosimov N.S. (2023). ELEKTRON JADVAL
MODELINING TAVSIFLANISHI. PEDAGOGS Jurnali, 30(3), 67–73.
2.
TA’LIMDA
DASTURLASH
JARAYONINI
BAHOLASHGA
ASOSLANGAN
AVTOMATLASHTIRILGAN
TIZIMNI
TADBIQ
ETISH
Normatov N.K., Choryorqulov G‘.H., Zamonaviy innovatsion tadqiqotlarning dolzarb
muammolari va rivojlanish tendensiyalari: yechimlar va istiqbollar mavzusidagi
Respublika ilmiy-texnik anjumani-2023, 20-24-betlar.
3.
Javlon X. et al. Классификатор движения рук с использованием
биомиметического распознавания образов с помощью сверточных нейронных
сетей с методом динамического порога для извлечения движения с
использованием датчиков EF //Journal of new century innovations. – 2022. – Т. 19.
– №. 6. – С. 352-357.
4.
Юсупович Ҳ. Ж., Эргашев С. Б. Ў. МAКТAБ ЎҚУВЧИЛAPИДA
AXБOPOТ БИЛAН ИШЛAШ КOМПEТEНЦИЯСИНИ PИВOЖЛAНТИPИШИ
МОДЕЛИ
//JOURNAL
OF
INNOVATIONS
IN
SCIENTIFIC
AND
EDUCATIONAL RESEARCH. – 2022. – Т. 2. – №. 13. – С. 189-194.
5.
Obid o‘g A. S. J. et al. Numpy Library Capabilities. Vectorized Calculation
In Numpy Va Type Of Information //Eurasian Research Bulletin. – 2022. – Т. 15. – С.
132-137.
6.
Tavboyev Sirojiddin Akhbutayevich, Mamaraimov Abror Kamoliddin ugli,
and Karshibaev Nizomiddin Abdumalikovich, “Algorithms for Selecting the Contour
Lines of Images Based on the Theory of Fuzzy Sets”, TJET, vol. 15, pp. 31–40, Dec.
2022.
DECISION TREE CLASSIFICATION IN MACHINE LEARNING AND
HYPERPARAMETERS
Salimov Jamshid Obid oʻgʻli
Jizzakh branch of National University of Uzbekistan
Abylayeva Akbota Muhamediyarovna
Eurasian National University named after L.N. Gumilyov
Annotation:
Machine learning algorithms play a crucial role in extracting
valuable insights from data, enabling businesses and researchers to make informed
decisions. One such algorithm is the decision tree, which is widely used for
classification tasks. Decision tree classification utilizes a tree-like model of decisions
and their potential consequences, making it an intuitive and powerful tool for solving
complex problems. In this article, a model that determines which drug is suitable for a
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patient with a certain disease is created using the Decision tree algorithm. This problem
is multi-class classification (multiclass classification) help score consolidation.
Alternatively, how function, domain, and hyperparameters simplify decision tree
models are explored.
Key words:
model, dataset, test set, training set, hyperparameters,
classification,
prediction, decision tree, multiclass classification
.
The purpose of decision tree classification is to divide a dataset into
homogeneous groups based on input features, leading to the assignment of categorical
labels to new, unseen instances. This algorithm mimics human decision-making
processes by forming a tree structure where each internal node represents a decision
based on a feature, and each leaf node represents a class label.
Decision trees are versatile and find application in various domains. They are
widely used in finance for credit scoring, fraud detection, and risk assessment. In
healthcare, decision trees aid in disease diagnosis and treatment prediction.
Additionally, decision trees are valuable in customer segmentation, sentiment analysis,
and recommendation systems.
The decision tree algorithm involves a series of steps to construct an optimal tree
structure. It begins with selecting the best feature from the dataset that effectively
divides the instances into distinct classes. This process is repeated recursively for each
subset of instances until the tree is fully grown. The algorithm uses measures such as
information gain, gain ratio, or Gini index to evaluate the feature's effectiveness at
splitting the data.
Hyperparameters are settings that control the behavior of the machine learning
algorithm. In the context of decision trees, hyperparameters influence the tree's
structure and complexity. Tuning these hyperparameters can simplify the decision tree
model and improve its performance.
One common hyperparameter is the maximum depth of the tree, which limits the
number of decision nodes and reduces complexity. Lowering the maximum depth helps
avoid overfitting. Another crucial hyperparameter is the minimum number of samples
required to split an internal node. Increasing this value prevents the creation of small
branches that may lead to overfitting.
Additionally, decision tree models can be regularized using hyperparameters like
minimum impurity decrease or maximum number of leaf nodes. These parameters
control the growth of the tree by setting thresholds for stopping the splitting process.
Decision Tree Algoritmi
Medical data is collected for research. This data (DataSet) is about patients
suffering from the same disease. During the course of treatment, one of the 5 different
drugs was given to the patient.
The goal is to create a model using the Decision tree algorithm that determines
which drug may be suitable for a future patient with the same disease. This problem is
solved using multiclass classification.
The graphic below is an example of how a Decision Tree works.
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How to build a Decision Tree model
•
Select a column of the dataset
•
We consider column importance in data partitioning
•
We split the data by the best column
•
We repeat the above steps.
determines which division leads to a better result.
https://journal.jbnuu.
uz/index.php/ijcstr/article/view/579
The necessary libraries and modules for the Decision Tree algorithm are called.
The required dataset is called to build the model.
https://raw.githubusercontent.com/JamshidSalimov/Ai-Fayls/master/drug200.csv
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Textual data is converted to digital form.
The predictor and original values are extracted from the dataset and equated to
the X and y variables
Dataset is split into train_set and test_set using train_test_split() module (60%
train_set, 40% test_set)
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The Decision Tree module is called and trained based on the Dataset.
Predictive values are found
Based on the predicted values, the model is evaluated
Model performance is evaluated using the classification_report() module based
on actual values and predicted values
It is also possible to calculate these values separately
It is also possible to evaluate in matrix form using the confusion_matrix()
module.
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Cross-validation of the model may give better results
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Based on the values, the Decision Tree algorithm graph was drawn.
Hyperparameters`min_impurity_decrease' - defines how "clean" the result will
be. The default value is 0
Using the max_dpth parameter, it is possible to control tree branches, that is, tree
branch layers
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min_samp_leaf The number of products to produce a leaf node (final leaf node).
Comparing graphs of the Decision Tree model, using hyperparameters
significantly simplifies the model, and the effect on model accuracy is not significant.
References:
1. Amrullayevich K. A., Obid o'g'li S. J. ELEKTRON TALIM MUHITIDA
TALABALARDA
AXBOROT
BILAN
ISHLASH
KOMPETENTLIKNI
SHAKLLANTIRISH //International Journal of Contemporary Scientific and Technical
Research. – 2022. – С. 641-645.
2. Obid o’g A. S. J. et al. Numpy Library Capabilities. Vectorized Calculation
In Numpy Va Type Of Information //Eurasian Research Bulletin. – 2022. – Т. 15. – С.
132-137.
3. Javlon X. et al. Классификатор движения рук с использованием
биомиметического распознавания образов с помощью сверточных нейронных
сетей с методом динамического порога для извлечения движения с
использованием датчиков EF //Journal of new century innovations. – 2022. – Т. 19.
– №. 6. – С. 352-357.
79
4.
Фитратович В. и др. МАТЕМАТИЧЕСКАЯ МОДЕЛЬ МНОГОФАЗНОЙ
ФИЛЬТРАЦИИ В НЕФТЕГАЗОВОМ ПЛАСТЕ ПРИ ЕГО ЗАВОДНЕНИИ
//INTERNATIONAL CONFERENCES ON LEARNING AND TEACHING. – 2022.
– Т. 1. – №. 4. – С. 520-525.
5.
Jamshid S. ENTROPY EVALUATION CRITERION IN DECISION TREE
ALGORITHM EVALUATION //International Journal of Contemporary Scientific and
Technical Research. – 2023. – С. 236-239.
FINGER PRINT-BASED ATTENDANCE SYSTEM
Sherbaev Javokhir Ravshan ugli,
Abdurakhmanov Ravshan Anarbayevich
Jizzakh branch of National University of Uzbekistan
Annotation:
The Fingerprint-Based Attendance System has emerged as a robust
and secure method for accurately recording attendance in various organizations and
educational institutions. This research paper explores the development,
implementation, and evaluation of such a system, highlighting its advantages,
challenges, and potential future enhancements. Through a combination of literature
review and practical experimentation, this paper aims to provide insights into the
effectiveness and reliability of fingerprint-based attendance systems.
Keywords:
Fingerprint, Attendance Management, Authentication.
Attendance tracking is a crucial aspect of organizational management and
educational institutions. Traditional methods of taking attendance, such as manual
paper-based systems or card swiping, have proven to be inefficient and susceptible to
fraud. In contrast, fingerprint-based attendance systems offer a more secure, accurate,
and convenient solution.
