Decision tree model
View article: THE IMPACT OF CATEGORICAL DATA ENCODING METHODS ON ARTIFICIAL INTELLIGENCE ALGORITHMS
THE IMPACT OF CATEGORICAL DATA ENCODING METHODS ON ARTIFICIAL INTELLIGENCE ALGORITHMS Open
This study analyzes the effectiveness of categorical data encoding methods in artificial intelligence algorithms. The research examines the operational characteristics and impact on results of four widely used encoding techniques—Label Enc…
View article: THE IMPACT OF CATEGORICAL DATA ENCODING METHODS ON ARTIFICIAL INTELLIGENCE ALGORITHMS
THE IMPACT OF CATEGORICAL DATA ENCODING METHODS ON ARTIFICIAL INTELLIGENCE ALGORITHMS Open
This study analyzes the effectiveness of categorical data encoding methods in artificial intelligence algorithms. The research examines the operational characteristics and impact on results of four widely used encoding techniques—Label Enc…
View article: DATA MINING–DRIVEN STUDENT PERFORMANCE PREDICTION: A SYMMETRIC UNCERTAINTY-BASED COMPARATIVE ANALYSIS OF DECISION TREE AND SUPPORT VECTOR MACHINE MODELS
DATA MINING–DRIVEN STUDENT PERFORMANCE PREDICTION: A SYMMETRIC UNCERTAINTY-BASED COMPARATIVE ANALYSIS OF DECISION TREE AND SUPPORT VECTOR MACHINE MODELS Open
View article: Classification and regression tree model for diabetes prediction
Classification and regression tree model for diabetes prediction Open
Diabetes mellitus is characterized by excessive blood glucose that occurs when the pancreas malfunctions while producing insulin. High blood glucose levels can cause chronic damage to organs, particularly the eyes and kidneys. Diabetes pre…
View article: DATA MINING–DRIVEN STUDENT PERFORMANCE PREDICTION: A SYMMETRIC UNCERTAINTY-BASED COMPARATIVE ANALYSIS OF DECISION TREE AND SUPPORT VECTOR MACHINE MODELS
DATA MINING–DRIVEN STUDENT PERFORMANCE PREDICTION: A SYMMETRIC UNCERTAINTY-BASED COMPARATIVE ANALYSIS OF DECISION TREE AND SUPPORT VECTOR MACHINE MODELS Open
View article: Video - A Decision Tree-Based Data Mining Approach to Estimating Dental Tourists' Satisfaction State
Video - A Decision Tree-Based Data Mining Approach to Estimating Dental Tourists' Satisfaction State Open
This study introduces a data mining approach to estimate dental tourists’ satisfaction. In detail, the study combines statistical techniques and a decision tree model to ensure a complete estimation pipeline. The study was conducted using …
View article: Video - A Decision Tree-Based Data Mining Approach to Estimating Dental Tourists' Satisfaction State
Video - A Decision Tree-Based Data Mining Approach to Estimating Dental Tourists' Satisfaction State Open
This study introduces a data mining approach to estimate dental tourists’ satisfaction. In detail, the study combines statistical techniques and a decision tree model to ensure a complete estimation pipeline. The study was conducted using …
View article: A Decision Tree-Based Data Mining Approach to Estimating Dental Tourists' Satisfaction State
A Decision Tree-Based Data Mining Approach to Estimating Dental Tourists' Satisfaction State Open
This study introduces a data mining approach to estimate dental tourists’ satisfaction. In detail, the study combines statistical techniques and a decision tree model to ensure a complete estimation pipeline. The study was conducted using …
View article: A Decision Tree-Based Data Mining Approach to Estimating Dental Tourists' Satisfaction State
A Decision Tree-Based Data Mining Approach to Estimating Dental Tourists' Satisfaction State Open
This study introduces a data mining approach to estimate dental tourists’ satisfaction. In detail, the study combines statistical techniques and a decision tree model to ensure a complete estimation pipeline. The study was conducted using …
View article: Decision tree confusion matrix | best resampler: SMOTE.
Decision tree confusion matrix | best resampler: SMOTE. Open
Decision tree confusion matrix | best resampler: SMOTE.
View article: Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection
Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection Open
Error detection (ED), which aims to identify incorrect or inconsistent cell values in tabular data, is important for ensuring data quality. Recent state-of-the-art ED methods leverage the pre-trained knowledge and semantic capability embed…
View article: Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection
Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection Open
Error detection (ED), which aims to identify incorrect or inconsistent cell values in tabular data, is important for ensuring data quality. Recent state-of-the-art ED methods leverage the pre-trained knowledge and semantic capability embed…
View article: Decision Trees Model Performance After Fine-Tuning.
Decision Trees Model Performance After Fine-Tuning. Open
Decision Trees Model Performance After Fine-Tuning.
View article: An example resolved LMM analysis decision tree for experiment A & B.
An example resolved LMM analysis decision tree for experiment A & B. Open
Here solid lines denote variables to be considered and grey boxes (dotted lines) denote decision criteria. Note: 1) Crossed out options are for example only, all options are available in real analysis tree. 2) For display purposes only inc…
View article: Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature Interactions
Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature Interactions Open
Decision trees are fundamental machine learning models known for their interpretability and efficiency. However, standard decision tree algorithms, such as CART and C4.5, predominantly rely on axis-aligned splits, which limit their ability…
View article: Accuracy of Decision Trees Model Performance with Different Max_Depth After Running Five Cross-Validation.
Accuracy of Decision Trees Model Performance with Different Max_Depth After Running Five Cross-Validation. Open
Accuracy of Decision Trees Model Performance with Different Max_Depth After Running Five Cross-Validation.
View article: Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature Interactions
Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature Interactions Open
Decision trees are fundamental machine learning models known for their interpretability and efficiency. However, standard decision tree algorithms, such as CART and C4.5, predominantly rely on axis-aligned splits, which limit their ability…
View article: Machine Learning-Aided Test Pattern Generation for VLSI Circuits: A Decision Tree Regressor Approach
Machine Learning-Aided Test Pattern Generation for VLSI Circuits: A Decision Tree Regressor Approach Open
As the demand for complex IC, high-performance computing increases, IC technology needs to evolve quickly. It relies on the technology node shrinking. Due to the highly complex nature of the chips, designing and testing circuits becomes an…
View article: Data Sheet 1_An interpretable 18F-FDG PET/CT-based radiomics model for predicting sub-3cm solitary adrenal metastases in cancer patients.docx
Data Sheet 1_An interpretable 18F-FDG PET/CT-based radiomics model for predicting sub-3cm solitary adrenal metastases in cancer patients.docx Open
Purpose To evaluate the potential of an interpretable radiomics model based on 18F-FDG PET/CT for predicting adrenal metastases (AMs) in cancer patients with indeterminate adrenal nodules. Materials and methods A total of 177 pa…
View article: Валідація архітектурних гіпотез у нейронних деревах рішень за допомогою пошуку нейронних архітектур
Валідація архітектурних гіпотез у нейронних деревах рішень за допомогою пошуку нейронних архітектур Open
This article introduces an automated and unbiased framework for validating architectural hypotheses for neural network models, with a particular focus on Neural Decision Trees (NDTs). The proposed methodology employs Neural Architecture Se…
View article: Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning
Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning Open
Objective We aimed to develop an interpretable model to predict the mortality risk for severe pneumonia patients. Methods The study retrospectively employed data from severe pneumonia patients at two hospitals as the training set for the m…
View article: Predicting House Prices: A Machine Learning Exploration with Decision Trees and Random Forests
Predicting House Prices: A Machine Learning Exploration with Decision Trees and Random Forests Open
In the dynamic real estate market, accurate house price prediction is crucial for buyers, sellers, and investors, yet traditional statistical methods often fail to capture the complex non-linear relationships between multiple factors and p…
View article: Table 1_Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning.docx
Table 1_Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning.docx Open
Objective We aimed to develop an interpretable model to predict the mortality risk for severe pneumonia patients. Methods The study retrospectively employed data from severe pneumonia patients at two hospitals as the training set for the m…
View article: An Infinite BART model
An Infinite BART model Open
Bayesian additive regression trees (BART) are popular Bayesian ensemble models used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpr…
View article: Global Sustainable Energy Analysis Using Data-Driven Methods and Machine Learning
Global Sustainable Energy Analysis Using Data-Driven Methods and Machine Learning Open
This study presents a comprehensive analysis of global sustainable energy trends using statistical techniques and machine learning methods. The research evaluates long-term patterns in renewable energy consumption, fossil fuel dependence, …
View article: Mathematica software for modeling long-term tree growth curves indirectly, when tree ages are unknown
Mathematica software for modeling long-term tree growth curves indirectly, when tree ages are unknown Open
The Mathematica notebook "Long-term growth curves of trees with unknown ages (Ricker Nov 2025).nb" carries out all calculations that are developed in the article "Modeling long-term tree growth curves indirectly with piecewise linear regre…
View article: Unified Computational Universe Terminal Object:\\Discrete Complexity Geometry, Information Geometry,\\Multi-Observer Causal Network, and Capability--Risk Structure
Unified Computational Universe Terminal Object:\\Discrete Complexity Geometry, Information Geometry,\\Multi-Observer Causal Network, and Capability--Risk Structure Open
This paper constructs on foundation of previous ``computational universe'' series works a unified computational universe terminal object with explicit categorical meaning. Previous works have axiomatized computational universe as four-tupl…
View article: Geometric Complexity Hierarchy and Complexity Classes\\in Computational Universe:\\Volume Growth, Curvature, and Computability Boundaries\\Under Unified Time Scale
Geometric Complexity Hierarchy and Complexity Classes\\in Computational Universe:\\Volume Growth, Curvature, and Computability Boundaries\\Under Unified Time Scale Open
In previous systematic studies of ``computational universe'' U_{comp} = (X,T,C,I), we have successively constructed discrete complexity geometry, discrete information geometry, control manifold (M,G) induced by unified time scale, task inf…
View article: Enhancing the Security of High-Responsibility Information Systems Through Fault Tree Modeling
Enhancing the Security of High-Responsibility Information Systems Through Fault Tree Modeling Open
With the advancement of technology, high-dependability information systems have become indispensable for the efficient operation of activities in strategic sectors, among which communications play a crucial role. This paper aims to ensure …
View article: Geometric Complexity Hierarchy and Complexity Classes\\in Computational Universe:\\Volume Growth, Curvature, and Computability Boundaries\\Under Unified Time Scale
Geometric Complexity Hierarchy and Complexity Classes\\in Computational Universe:\\Volume Growth, Curvature, and Computability Boundaries\\Under Unified Time Scale Open
In previous systematic studies of ``computational universe'' U_{comp} = (X,T,C,I), we have successively constructed discrete complexity geometry, discrete information geometry, control manifold (M,G) induced by unified time scale, task inf…