Determination of Algorithms Making Balance Between Accuracy and Comprehensibility in Churn Prediction Setting

2011 ◽  
Vol 1 (2) ◽  
pp. 39-54 ◽  
Author(s):  
Hossein Abbasimehr ◽  
Mohammad Jafar Tarokh ◽  
Mostafa Setak
Keyword(s):  
Logistic Regression ◽  
Predictive Modeling ◽  
Prediction Models ◽  
Rule Induction ◽  
Parameter Setting ◽  
Churn Prediction ◽  
Training Set ◽  
Rule Based ◽  
The Past

Predictive modeling is a useful tool for identifying customers who are at risk of churn. An appropriate churn prediction model should be both accurate and comprehensible. However, reviewing the past researches in this context shows that much attention is paid to accuracy of churn prediction models than comprehensibility of them. This paper compares three different rule induction techniques from three categories of rule based classifiers in churn prediction context. Furthermore logistic regression (LR) and additive logistic regression (ALR) are used. After parameter setting, eight distinctive algorithms, namely C4.5, C4.5 CP, RIPPER, RIPPER CP, PART, PART CP, LR, and ALR, are obtained. These algorithms are applied on an original training set with the churn rate of 30% and another training set with the churn rate of 50%. Only the models built by applying these algorithms on a training set with the churn rate of 30% make balance between accuracy and comprehensibility. In addition, the results of this paper show that ALR can be an excellent alternative for LR, when models only from accuracy perspective are evaluated.

Determination of Algorithms Making Balance Between Accuracy and Comprehensibility in Churn Prediction Setting

2013 ◽  
pp. 116-131
Author(s):  
Hossein Abbasimehr ◽  
Mohammad Jafar Tarokh ◽  
Mostafa Setak
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Predictive Modeling ◽  
Prediction Models ◽  
Rule Induction ◽  
Original Training ◽  
Churn Prediction ◽  
Training Set ◽  
Rule Based

Predictive modeling is a useful tool for identifying customers who are at risk of churn. An appropriate churn prediction model should be both accurate and comprehensible. However, reviewing the past researches in this context shows that much attention is paid to accuracy of churn prediction models than comprehensibility of them. This paper compares three different rule induction techniques from three categories of rule based classifiers in churn prediction context. Furthermore logistic regression (LR) and additive logistic regression (ALR) are used. After parameter setting, eight distinctive algorithms, namely C4.5, C4.5 CP, RIPPER, RIPPER CP, PART, PART CP, LR, and ALR, are obtained. These algorithms are applied on an original training set with the churn rate of 30% and another training set with the churn rate of 50%. Only the models built by applying these algorithms on a training set with the churn rate of 30% make balance between accuracy and comprehensibility. In addition, the results of this paper show that ALR can be an excellent alternative for LR, when models only from accuracy perspective are evaluated.

scholarly journals Machine Learning Implementation in Electronic Commerce for Churn Prediction of End User

2021 ◽  
Vol 10 (5) ◽  
pp. 20-25
Author(s):  
Neha Sharma* ◽  
Aayush Raj ◽  
Vivek Kesireddy ◽  
Preetham Akunuri
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Recurrent Neural Network ◽  
Random Forest Classifier ◽  
Churn Prediction ◽  
End User ◽  
The Past ◽  
Neural Organization ◽  
The Future ◽  
Points Of View

Client conduct can be addressed from numerous points of view. The client's conduct is distinctive in various circumstances will give his concept of client conduct. From an overall viewpoint, the conduct of the client, or rather any individual around there, is taken to be irregular. When noticed distinctly, it is regularly seen that the future conduct of an individual can rely upon different variables of the current circumstance just as the conduct in past circumstances. This examination establishes the forecast of client beat, for example regardless of whether the client will end buying from the purchaser or not, which relies upon different components. We have chipped away at two sorts of client information. To start with, that is reliant upon the current elements which don't influence the past or future buys. Second, a period arrangement information which gives us a thought of how the future buys can be identified with the buys before. Logistic Regression, Random Forest Classifier, Artificial neural organization, and Recurrent Neural Network has been carried out to find the connections of the agitate with different factors and order the client beat productively. The correlation of calculations demonstrates that the aftereffects of Logistic Regression were somewhat better for the principal Dataset. The Recurrent Neural Network model, which was applied to the time-arrangement dataset, additionally gave better outcomes.

Building comprehensible customer churn prediction models with advanced rule induction techniques

2011 ◽  
Vol 38 (3) ◽  
pp. 2354-2364 ◽  
Author(s):  
Wouter Verbeke ◽  
David Martens ◽  
Christophe Mues ◽  
Bart Baesens
Keyword(s):  
Prediction Models ◽  
Rule Induction ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

Transmission Electron Microscopy of Protein Macromolecules

1971 ◽  
Vol 29 ◽  
pp. 424-425
Author(s):  
Henry S. Slayter
Keyword(s):  
Biological Systems ◽  
Metal Vapor ◽  
Resolving Power ◽  
Electron Microscopic ◽  
Chemical Methods ◽  
Molecular Weights ◽  
The Past ◽  
Physical Chemical ◽  
Transmission Electron

Electron microscopic methods have been applied increasingly during the past fifteen years, to problems in structural molecular biology. Used in conjunction with physical chemical methods and/or Fourier methods of analysis, they constitute powerful tools for determining sizes, shapes and modes of aggregation of biopolymers with molecular weights greater than 50, 000. However, the application of the e.m. to the determination of very fine structure approaching the limit of instrumental resolving power in biological systems has not been productive, due to various difficulties such as the destructive effects of dehydration, damage to the specimen by the electron beam, and lack of adequate and specific contrast. One of the most satisfactory methods for contrasting individual macromolecules involves the deposition of heavy metal vapor upon the specimen. We have investigated this process, and present here what we believe to be the more important considerations for optimizing it. Results of the application of these methods to several biological systems including muscle proteins, fibrinogen, ribosomes and chromatin will be discussed.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

Crime and Punishment

2017 ◽  
Author(s):  
Richard Adelstein
Keyword(s):  
Criminal Liability ◽  
Court Case ◽  
Efficient Allocation ◽  
Two Stage ◽  
Crime And Punishment ◽  
The Past ◽  
Ex Post ◽  
Ex Ante ◽  
Supreme Court Case

This chapter elaborates the operation of criminal liability by closely considering efficient crimes and the law’s stance toward them, shows how its commitment to proportional punishment prevents the probability scaling that systemically efficient allocation requires, and discusses the procedures that determine the actual liability prices imposed on offenders. Efficient crimes are effectively encouraged by proportional punishment, and their nature and implications are examined. But proportional punishment precludes probability scaling, and induces far more than the systemically efficient number of crimes. Liability prices that match the specific costs imposed by the offender at bar are sought through a two-stage procedure of legislative determination of punishment ranges ex ante and judicial determination of exact prices ex post, which creates a dilemma: whether to price crimes accurately in the past or deter them accurately in the future. An illustrative Supreme Court case bringing all these themes together is discussed in conclusion.

Sampling, Preservation and Counting of Samples II: Zooplankton

2017 ◽  
Author(s):  
Peter H. Wiebe ◽  
Ann Bucklin ◽  
Mark Benfield
Keyword(s):  
Genetic Analysis ◽  
High Speed ◽  
Morphological Analysis ◽  
Taxonomic Composition ◽  
Sampling Techniques ◽  
Sample Analysis ◽  
Sample Preservation ◽  
The Past ◽  
Plankton Collection

This chapter reviews traditional and new zooplankton sampling techniques, sample preservation, and sample analysis, and provides the sources where in-depth discussion of these topics is addressed. The net systems that have been developed over the past 100+ years, many of which are still in use today, can be categorized into eight groups: non-opening/closing nets, simple opening/closing nets, high-speed samplers, neuston samplers, planktobenthos plankton nets, closing cod-end samplers, multiple net systems, and moored plankton collection systems. Methods of sample preservation include preservation for sample enumeration and taxonomic morphological analysis, and preservation of samples for genetic analysis. Methods of analysis of zooplankton samples include determination of biomass, taxonomic composition, and size by traditional methods; and genetic analysis of zooplankton samples.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

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