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Elder Research is a recognized leader in the science, practice, and technology of advanced analytics. Topics on our blog cover analytics tips, analytical modeling, data and text mining tools, data visualization, analytics best practices, case studies, etc. to provide business leaders with actionable information on real world analytics problems.
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Improve Predictive Model Performance With EnsemblesIn my previous blog, Ensembles and Regularization – Analytics Superheroes, I reviewed the many advantages of model ensembles including removing “noise” variables, generalizing better than single component models, and reducing sensitivity to outliers. In this article I take a deeper dive into the attributes and applications of model ensembles, and explore potential downsides to provide context for when to use...
In my previous blog, Ensembles and Regularization – Analytics Superheroes, I reviewed the many advantages of model ensembles including removing “noise” variables, generalizing better than single component models, and reducing sensitivity to outliers.
In this article I take a deeper dive into the attributes and applications of model ensembles, and explore potential downsides to provide context for when to use them.
Trends in Natural Language Processing
Deep Neural Networks (DNN) have radically changed the landscape of state-of-the-art performance in Natural Language Processing (NLP) within recent years. These versatile models are being used in many applications including text classification, language creation, question answering, image captioning, language translation, named entity recognition, and speech recognition. The state-of-the-art is changing quickly, sometimes leading to large leaps in performance with the release of new...
Deep Neural Networks (DNN) have radically changed the landscape of state-of-the-art performance in Natural Language Processing (NLP) within recent years. These versatile models are being used in many applications including text classification, language creation, question answering, image captioning, language translation, named entity recognition, and speech recognition. The state-of-the-art is changing quickly, sometimes leading to large leaps in performance with the release of new architectures. In October of 2018 Google released BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding which performed best in 11 different NLP benchmarks upon release. Since then, there have been many more models adding new components or tweaking the approach. In this article we’ll review some of the traditional machine learning methods used in deep learning and new trends such as Transfer Learning and Transformers to provide a foundation no matter what model is currently leading.
Modeling Outcomes: Explain or Predict
A casual user of machine learning methods like CART or Naive Bayes is accustomed to evaluating a model by measuring how well it predicts new data. When examining the output of statistical models, they are often flummoxed by the profusion of assessment metrics. Typical multiple linear regression output will contain, in addition to a distribution of errors (residuals) and root mean squared error (RMSE), values such as R-squared, adjusted R-squared, t-statistics, F-statistics, P-values,...
A casual user of machine learning methods like CART or Naive Bayes is accustomed to evaluating a model by measuring how well it predicts new data. When examining the output of statistical models, they are often flummoxed by the profusion of assessment metrics. Typical multiple linear regression output will contain, in addition to a distribution of errors (residuals) and root mean squared error (RMSE), values such as R-squared, adjusted R-squared, t-statistics, F-statistics, P-values, degrees of freedom, at a minimum, plus more.
Making Good Use of Negative Space in Machine Learning
Data Scientists frequently build Machine Learning models to discover interesting (rare) events in data. These events can be valuable (e.g., customer purchases), costly (e.g., fraud), or even dangerous (e.g., threat). Finding them is a “needle-in-a-haystack” challenge: the events are rare and hard to distinguish from the huge mass of overwhelmingly uninteresting cases recorded. To differentiate rare from normal events it helps to have a good understanding of normal behavior. But, how well...
Data Scientists frequently build Machine Learning models to discover interesting (rare) events in data. These events can be valuable (e.g., customer purchases), costly (e.g., fraud), or even dangerous (e.g., threat). Finding them is a “needle-in-a-haystack” challenge: the events are rare and hard to distinguish from the huge mass of overwhelmingly uninteresting cases recorded. To differentiate rare from normal events it helps to have a good understanding of normal behavior. But, how well do you actually know the haystack?
Ways Machine Learning Models Fail: Missing Causes
I have identified five primary reasons why analytical models fail: Poor Organizational Support Missing Causes Model Overfit Data Problems False Beliefs In this post, we will consider how and why missing causes in the data for training a model may result in incorrect inferences or failures....
I have identified five primary reasons why analytical models fail:
- Poor Organizational Support
- Missing Causes
- Model Overfit
- Data Problems
- False Beliefs
In this post, we will consider how and why missing causes in the data for training a model may result in incorrect inferences or failures.
Leveraging Data Analytics to Increase ROI
Reluctance to trust and rely on machine-based decisions is widespread. That is understandable; how can one be sure the automated decision system takes into account all the factors it should? Employees struggle first to learn the new technology, and then after making great progress and producing a promising model, decision-makers can still prove extremely reluctant to risk a new approach, no matter how well tests reveal its effectiveness. Still, in today’s competitive work environment,...
Reluctance to trust and rely on machine-based decisions is widespread. That is understandable; how can one be sure the automated decision system takes into account all the factors it should? Employees struggle first to learn the new technology, and then after making great progress and producing a promising model, decision-makers can still prove extremely reluctant to risk a new approach, no matter how well tests reveal its effectiveness. Still, in today’s competitive work environment, having a positive relationship with machines is essential to increasing profits and building return on investment (ROI).
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