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Make Your Investment in Analytic Technology Pay Off With Decision Requirements Modeling

Decision Management Solutions

Like many enterprises, you’ve likely made a hefty investment in analytic technology—from interactive dashboards and advanced visualization tools to data mining, predictive analytics, machine learning (ML), and artificial intelligence (AI). 1 MIT Sloan Management Review September 06, 2017. We can show you how to accomplish this.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. Machine Learning, 57–78.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

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Magnificent Mobile Website And App Analytics: Reports, Metrics, How-to!

Occam's Razor

But why blame others, in this post let's focus on one important reason whose responsibility can be squarely put on your shoulders and mine: Measurement. Create a distinct mobile website and mobile app measurement strategies. Remember my stress earlier on measuring micro-outcomes?). Framing the Opportunity.

Metrics 143
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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. Ramp-up solution: measure epoch and condition on its effect If one wants to do full traffic ramp-up and use data from all epochs, they must use an adjusted estimator to get an unbiased estimate of the average reward in each arm.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox. See Wei et al. References.

Modeling 139
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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

While we work on programs to avoid such inconvenience , AI and machine learning are revolutionizing the way we interact with our analytics and data management while increment in security measures must be taken into account. It’s an extension of data mining which refers only to past data.