Predicting Employee Attrition using Supervised Learning Classification Models

Purpose The purpose of this study is to develop tree-based binary classification models to predict the likelihood of employee attrition based on firm cultural and management attributes. Design/methodology/approach A data set of resumes anonymously submitted through Glassdoor’s online portal is used in tandem with public company review information to fit decision tree, random forest and gradient boosted tree models to predict the probability of an employee leaving a firm during a job transition. Findings Random forest and decision tree methods are found to be the strongest attrition prediction models. In addition, compensation, company culture and senior management performance play a primary role in an employee’s decision to leave a firm. Practical implications This study may be used by human resources staff to better understand factors which influence employee attrition. In addition, techniques developed in this study may be applied to company-specific data sets to construct customized attrition models. Originality/value This study contains several novel contributions which include exploratory studies such as industry job transition percentages, distributional comparisons between factors strongly contributing to employee attrition between those who left or stayed with the firm and the first comprehensive search over binary classification models to identify which provides the strongest predictive performance of employee attrition.

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Supervised Learning Classification Models for Prediction of Plant Virus Encoded RNA Silencing Suppressors

PLoS ONE ◽

10.1371/journal.pone.0097446 ◽

2014 ◽

Vol 9 (5) ◽

pp. e97446 ◽

Cited By ~ 7

Author(s):

Zeenia Jagga ◽

Dinesh Gupta

Keyword(s):

Supervised Learning ◽

Rna Silencing ◽

Plant Virus ◽

Classification Models

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Learning from noisy out-of-domain corpus using dataless classification

Natural Language Engineering ◽

10.1017/s1351324920000340 ◽

2020 ◽

pp. 1-31

Author(s):

Yiping Jin ◽

Dittaya Wanvarie ◽

Phu T. V. Le

Keyword(s):

Supervised Learning ◽

Text Classification ◽

Classification Method ◽

Classification Models ◽

Selection Methods ◽

Target Domain ◽

Data Set ◽

Depth Analysis ◽

Real World Applications ◽

Data Problem

Abstract In real-world applications, text classification models often suffer from a lack of accurately labelled documents. The available labelled documents may also be out of domain, making the trained model not able to perform well in the target domain. In this work, we mitigate the data problem of text classification using a two-stage approach. First, we mine representative keywords from a noisy out-of-domain data set using statistical methods. We then apply a dataless classification method to learn from the automatically selected keywords and unlabelled in-domain data. The proposed approach outperformed various supervised learning and dataless classification baselines by a large margin. We evaluated different keyword selection methods intrinsically and extrinsically by measuring their impact on the dataless classification accuracy. Last but not least, we conducted an in-depth analysis of the behaviour of the classifier and explained why the proposed dataless classification method outperformed supervised learning counterparts.

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Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3097 ◽

2020 ◽

Vol 6 (3) ◽

pp. 376-379

Author(s):

Seyed Amir Hossein Tabatabaei ◽

David Pedrosa ◽

Carsten Eggers ◽

Max Wullstein ◽

Urs Kleinholdermann ◽

...

Keyword(s):

Machine Learning ◽

Supervised Learning ◽

Machine Learning Techniques ◽

Disease Detection ◽

Classification Models ◽

Timed Up And Go ◽

Parkinson's Syndrome ◽

Parkinson’S Syndrome ◽

Specificity And Sensitivity ◽

Learning Techniques

AbstractIn this paper, the classification models for Idiopathic Parkinson's syndrome (iPS) detection through timed-up-and-go test performed on iPS-patients are given. The models are based on the supervised learning. The data are extracted via Myo gesture armband worn on two hands. The corresponding models are based on extracted features from signal data and raw signal data respectively. The achieved accuracy from both models are 0.91 and 0.93 with reasonable specificity and sensitivity.

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Unsupervised and Supervised Learning based Classification Models for Air Pollution Data

10.1109/gcat52182.2021.9587793 ◽

2021 ◽

Author(s):

Sandeep Kumar Sunori ◽

Pushpa Bhakuni Negi ◽

Pradeep Juneja ◽

M Niranjanamurthy ◽

P.G. Om Prakash ◽

...

Keyword(s):

Air Pollution ◽

Supervised Learning ◽

Classification Models

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To Improve Voice Recognition System using GMM and HMM Classification Models

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k2178.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 2724-2726

Keyword(s):

Speech Recognition ◽

Supervised Learning ◽

Hybrid Model ◽

Voice Recognition ◽

Recognition System ◽

Classification Model ◽

Time Series Classification ◽

Classification Models ◽

Learning Patterns ◽

The Individual

In this paper, the researcher study automatic speech recognition technology for the individual. We propose a new voice recognition system using a hybrid model GMM-HMM. HMM and GMM is a non-linear classification model. Each state in an HMM can be thought of as a GMM. HMM is consider observation for state. It is also known as time series classification model. In this model, samples have been trained independently and parameters consider jointly which provides better performance than other classification models. Speech recognition system consider two types of learning patterns such as supervised learning and unsupervised learning. In this context speaker dependent and speaker independent used for identifying the efficient and effective voice. In this paper researcher considered supervised learning model for recognize efficient voice. This new voice recognition system identifies incorrect phonemes and verifies the correctness of voice pronunciation. Using the GMM-HMM hybrid model produces better performance and effectiveness of voice

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