A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression

Martín-Baos, José Ángel; García-Ródenas, Ricardo; Rodríguez-Benítez, Luis

doi:10.1016/j.trpro.2021.11.009

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…Here, we use the PermutationImportance method in the Sklearn package to calculate the feature importance by randomizing all the features in the dataset. Then, we use Logistic Regression and KNN algorithm for training, and use PermutationImportance method to calculate the characteristic importance or contribution rate of these two algorithms to the target variable y respectively [9]. Then the feature importance weights are sorted respectively.…”

Section: Discussionmentioning

confidence: 99%

Classification and Prediction of Bank Marketing Activity by Machine Learning

Zhao

2023

HBEM

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As the competition in the financial industry intensifies, the customer experience has become the key to whether the financial institutions can continue to develop. As the traditional banking services are difficult to meet the needs of customers, more and more people begin to choose to trade through self-service channels or online banking. With the popularity of mobile devices, people are increasingly dependent on mobile banking and mobile payment. Banks analyze the customer behavior data to realize the effective interaction between customers and banks, which has become a hot issue in the financial industry. From the perspective of machine learning, this paper will analyze the ability of a bank customer to buy products, mainly including customer purchasing power analysis, product classification, customer stratification and other aspects. On this basis, this paper will use the decision tree algorithm to segment the customers with different purchasing power, so as to achieve precision marketing.

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Section: Discussionmentioning

confidence: 99%

Classification and Prediction of Bank Marketing Activity by Machine Learning

Zhao

2023

HBEM

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“…After gathering n selected choices among the suggested routes by the driver we estimate new weights using Maximal Likelihood Estimation. For this, we used the pylogit library [12].…”

Section: Benchmarksmentioning

confidence: 99%

Personalised Electric Vehicle Charging Stop Planning Through Online Estimators

Shafipour,

Stein,

Ahipasaoglu

2023

Preprint

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In this paper, we address the problem of finding charging stops while travelling in electric vehicles (EVs) using artificial intelligence (AI). Choosing a charging station is challenging, because drivers have very heterogeneous preferences in terms of how they trade off the features of various alternatives (for example, regarding the time spent driving, charging costs, waiting times at charging stations, and the facilities provided at the charging stations). The key problem here is eliciting the diverse preferences of drivers, assuming that these preferences are typically not fully known a priori, and then planning stops based on each driver's preferences. Our approach to solving this problem is to develop an intelligent personal agent that learns preferences gradually over multiple interactions. This study proposes a new technique that utilises a small-scale discrete choice experiment as a method of interacting with the driver in order to minimise the cognitive burden on the driver. Using this method, drivers are presented with a variety of routes with possible combinations of charging stops depending on the agent's latest belief about their preferences. In subsequent iterations, the personal agent will continue to learn and refine its belief about the driver's preferences, suggesting more personalised routes that are closer to the driver's preferences. Based on real preference data from EV drivers, we evaluate our novel algorithm and show that, after only a few queries, our method quickly converges to the optimal routes for EV drivers.

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“…Another way to improve ML algorithms is to create updated software packages and ML algorithms. For example, creating new Python libraries that enhance the ML models by modifying their equation [9]. The Python package called Py Kernel Logit [9] introduces the model Kernel Logistic Regression (KLR).…”

Section: Introductionmentioning

confidence: 99%

“…For example, creating new Python libraries that enhance the ML models by modifying their equation [9]. The Python package called Py Kernel Logit [9] introduces the model Kernel Logistic Regression (KLR). The package was created to meet the need for ML models in the transport industry.…”

Section: Introductionmentioning

confidence: 99%

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A fast and effective approach for classification medical data sets

Ivanov

Toleva

Hooper

2023

ijirss

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This study's objective is to offer a practical computer method for handling classification problems on large datasets. The aim of this study is to offer a practical computer approach for handling classification tasks on big datasets. We show that using Python’s built-in parameters to balance classes can improve the accuracy and the metrics of a classification task. We employ logistic regression, support vector machines, decision trees, and random forest classifier. We use the parameter “class_weight='balanced'” to run each classification model as well as stratified train/test splitting to ensure that relative class frequencies are approximately preserved in each train and set subsets. We use our methods on medical datasets because class imbalance is frequently a problem there. Our research shows that the proposed algorithms can improve the accuracy and classification metrics of the given medical datasets. We propose an effective and easy-to-apply alternative to improve the prediction ability of the presented classification models in medical datasets. We test an easily reproducible set-up where any classification model can be used to model imbalanced classes. The key tuning of the model lies in the stratified train/test split as well as the parameter “class weight='balanced'”. By combination of parameter tuning, better classification performance can be obtained in a quick and simple manner. It is simple and quick to replicate our algorithms to examine various medical datasets and determine which model best fits the data. It can be reproduced in biostatistical laboratories and by medical companies. Because it is simple to comprehend, medical researchers can swiftly review the information and determine the best course of action.

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A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression

Cited by 3 publications

References 6 publications

Classification and Prediction of Bank Marketing Activity by Machine Learning

Classification and Prediction of Bank Marketing Activity by Machine Learning

Personalised Electric Vehicle Charging Stop Planning Through Online Estimators

A fast and effective approach for classification medical data sets

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