Machine Learning Model Selection for Attrition Prediction Accuracy
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Solution Overview
Problem
Existing methods fail to effectively predict and manage employee attrition and apparatus wear, leading to fluctuations in customer expectations and operational inefficiencies.
Innovation Solution
A method for creating an improved machine learning model by selecting the best machine learning algorithm and data engineering technique, validated through a test dataset, to predict attrition levels in employees or apparatuses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning algorithms are tested with various data engineering techniques, then prediction accuracy for attrition is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the model development process into distinct phases: data engineering technique application, model training with multiple algorithms, hyperparameter tuning, and validation. This segmentation allows systematic evaluation of each component's contribution to prediction accuracy while managing overall system complexity through structured methodology.
Solution Approach 2:
The patent implements dynamic selection of machine learning algorithms and data engineering techniques based on performance metrics. The system adapts by selecting the best-performing algorithm and technique combinations through rigorous testing and validation, rather than using a fixed static approach, thereby optimizing prediction accuracy for attrition forecasting.
2Reliability
If hyperparameter tuning and validation on separate datasets are performed, then model reliability is improved, but training time and computational cost increase
Solution Approach 1:
The patent applies data engineering techniques and performs preliminary model training before final validation. By preparing data and training models in advance with multiple algorithms, the system establishes a robust foundation that reduces the need for extensive retraining and validation iterations, thereby managing time investment while ensuring model reliability.
Solution Approach 2:
The patent implements feedback mechanisms through validation on separate test datasets and hyperparameter tuning based on performance metrics. This feedback loop allows the system to identify the most reliable model configurations and adjust hyperparameters accordingly, ensuring model reliability while optimizing the balance between training time and validation thoroughness.
Data Source
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AI summary
The invention concerns a method of creating an improved model of machine learning, the method being carried out by a processing unit and comprising the steps of: - considering a plurality of machine learning algorithms and a training dataset, - creating a plurality of machine learning models by applying each machine learning algorithm on the training dataset, -selecting one of the machine learning models, - applying a plurality of data engineering techniques on the selected machine learning model to improve performance, - determining which data engineering techniques has the best performance; the data engineering technique which has the best performance associated with the selected machine learning model constitute the improved machine learning model, - applying the improved machine learning model on a test dataset to validate the performance of the improved machine learning model.