To Predict Customer Churn By Using Different Algorithms

Rahman, M D Sayedur; Alam, M D Sahrial; Hosen, M D Ikbal

doi:10.1109/dasa54658.2022.9765155

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…The telecommunications data utilized in this study were the Telco Customer Churn data (Telco data) from the Kaggle dataset [14]. This is a binary classi cation dataset that involves customer information with the label column indicating whether a customer left within the last month.…”

Section: Datasetsmentioning

confidence: 99%

Measuring the Prediction Difficulty of Individual Cases in a Dataset using Machine Learning

Kwon,

Greenberg,

Josephson

et al. 2023

Preprint

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Different levels of prediction difficulty are one of the key factors that researchers encounter when applying machine learning to data. Although previous studies have introduced various metrics for assessing the prediction difficulty of individual cases, these metrics require specific dataset preconditions. Additionally, evaluating these preconditions in real-world datasets can be challenging due to their diversity and complexity. In this paper, we propose three novel metrics for measuring the prediction difficulty of individual cases using fully-connected feedforward neural networks. The new metrics are designed to be universal metrics capable of calculating prediction difficulty in any dataset. The first metric is based on the complexity of the neural network needed to make a correct prediction. The second metric employs a pair of neural networks: one makes a prediction for a given case, and the other predicts whether the prediction made by the first model is likely to be correct. The third metric assesses the variability of the neural network’s predictions. We investigated these metrics using a variety of datasets, visualized their values, and compared them to existing metrics from the literature. The results demonstrate the effectiveness of our metrics across diverse datasets. We expect our metrics will provide researchers with a new perspective on understanding their datasets and applying machine learning in various fields.

show abstract

Section: Datasetsmentioning

confidence: 99%

Measuring the Prediction Difficulty of Individual Cases in a Dataset using Machine Learning

Kwon,

Greenberg,

Josephson

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Ensemble Learning for Churn Analysis: A Comprehensive Evaluation of Methods

Bharambe,

Ranjan,

Deshmukh

et al. 2024

Lecture Notes in Electrical Engineering

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Measuring the prediction difficulty of individual cases in a dataset using machine learning

Kwon,

Greenberg,

Josephson

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Different levels of prediction difficulty are one of the key factors that researchers encounter when applying machine learning to data. Although previous studies have introduced various metrics for assessing the prediction difficulty of individual cases, these metrics require specific dataset preconditions. In this paper, we propose three novel metrics for measuring the prediction difficulty of individual cases using fully-connected feedforward neural networks. The first metric is based on the complexity of the neural network needed to make a correct prediction. The second metric employs a pair of neural networks: one makes a prediction for a given case, and the other predicts whether the prediction made by the first model is likely to be correct. The third metric assesses the variability of the neural network’s predictions. We investigated these metrics using a variety of datasets, visualized their values, and compared them to fifteen existing metrics from the literature. The results demonstrate that the proposed case difficulty metrics were better able to differentiate various levels of difficulty than most of the existing metrics and show constant effectiveness across diverse datasets. We expect our metrics will provide researchers with a new perspective on understanding their datasets and applying machine learning in various fields.

show abstract

To Predict Customer Churn By Using Different Algorithms

Cited by 6 publications

References 11 publications

Measuring the Prediction Difficulty of Individual Cases in a Dataset using Machine Learning

Measuring the Prediction Difficulty of Individual Cases in a Dataset using Machine Learning

Ensemble Learning for Churn Analysis: A Comprehensive Evaluation of Methods

Measuring the prediction difficulty of individual cases in a dataset using machine learning

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