This study aims at providing a predictive analysis on customer churn in the telecommunication industry using machine learning algorithms. A hybrid model classification consisting of random forest (RF) and XGBoost classifiers as well as a voting/ranking system that combines both logics and decisions to...
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Feature attributions based on the Shapley value are popular for explaining machine learning models. However, their estimation is complex from both theoretical and computational standpoints. We disentangle this complexity into two main factors: the approach to removing feature information and the tractable...
Clustering algorithms work well for questions like: Which viewers like the same types of movies? Which printer models fail in the same way? Classification Classification algorithms use predictive calculations to assign data to preset categories. Classification algorithms are trained on input data, and ...
The technique of boosting a machine learning algorithm can improve its overall performance. Data scientists must understanddata preparationas a precursor to feeding data sets to machine learning models for analysis. Learn thesix steps involved in the data preparation process....
Anomaly detection algorithms are very useful for predictive maintenance. They’re also very helpful when paired with machine learning models. Many machine learning models will produce spurious, random results if they are presented with an input that isn’t in their training set. To avoid this, an...
Evolutionary algorithms demonstrated excellent capabilities in knowledge discovery, with good predictive accuracy of the discovered rules and high accuracy rate for the overall rule sets. Critical aspects in their application are the choice of the fitness function and the choice of the population. Most ...
We also use optional cookies for advertising, personalisation of content, usage analysis, and social media. By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with...
Fatigue analysis Nonlinear time-series analysis Model-based analysis such as residual computation, state estimation, and parameter estimation Predictive Maintenance Toolbox supplements functionality in other toolboxes such as Signal Processing Toolbox™ with functions for extracting signal-based or...
Furthermore, AI optimization algorithms can be used to develop predictive models for student performance and retention. By training machine learning models on historical student data, universities can identify the key factors that contribute to student success and predict the likelihood of a student drop...