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Implementation of LightGBM and Random Forest in Potential Customer Classification
TIERS Information Technology Journal, 2023 Classification is one of the data mining techniques that can be used to determine potential custumers. Previous research show that the boosting method, especially LGBM, produces the highest accuracy value of all models, namely 100%.
Laura Sari +3 more
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An Approximation Method for Fitted Random Forests
CoRR, 2022 Random Forests (RF) is a popular machine learning method for classification and regression problems. It involves a bagging application to decision tree models. One of the primary advantages of the Random Forests model is the reduction in the variance of the forecast.
openaire +3 more sources
Forest-RK: A New Random Forest Induction Method [PDF]
, 2008 In this paper we present our work on the parametrization of Random Forests (RF), and more particularly on the number K of features randomly selected at each node during the tree induction process. It has been shown that this hyperparameter can play a significant role on performance.
Simon Bernard 0001 +2 more
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Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution [PDF]
, 2006 Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers ...
Zeileis, Achim +11 more
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, 2022 Flow chart of classification using Random Forest algorithm (Source: https://www.section.io/engineering-education/introduction-to-random-forest-in-machine-learning/).
Pham Minh Hai (14201069) +7 more
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An AUC-based Permutation Variable Importance Measure for Random Forests [PDF]
, 2012 The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
Silke Janitza +5 more
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Method for Profile-Guided Optimization of Android Applications Using Random Forest
IEEE Access, 2022 When choosing a smartphone, many users are guided by the performance of smartphones and the speed of applications. Because it is difficult to measure the application speed directly, the speed of the application startup is considered and used for ...
Andrei Visochan +7 more
doaj +1 more source
World Electric Vehicle Journal, 2023 With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers’ purchase intentions. According to the data of customers’ vehicle experience evaluation and personal information,
Jian Chen +6 more
doaj +1 more source
Feature selection to increase the random forest method performance on high dimensional data
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 Random Forest is a supervised classification method based on bagging (Bootstrap aggregating) Breiman and random selection of features. The choice of features randomly assigned to the Random Forest makes it possible that the selected feature is not ...
Maria Irmina Prasetiyowati +2 more
doaj +1 more source
Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics [PDF]
, 2012 The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with ...
König, Inke R. +3 more
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