Remove 2015 Remove Measurement Remove Optimization Remove Statistics
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Gartner D&A Summit Bake-Offs Explored Flooding Impact And Reasons for Optimism!

Rita Sallam

Are there mitigation strategies that show reasons for optimism? For the vendors that participate in the Bake-Off and Show Floor Showdowns, it is in equal measure fun and extremely stressful. The results showing the impact of flooding, predictions for the future and reasons for optimism were fascinating.

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Remove Your Rose Tinted Glasses: Data Visualizations Designed to Mislead

datapine

From political issues to sports statistics and the recent report you received on the ROI of your company blog, the internet as well as informational reports are flooded with examples of misleading data visualization. If you want to go deeper into the topic, take a look at our misleading statistics blog post.

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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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Fitting Support Vector Machines via Quadratic Programming

Domino Data Lab

Support Vector Machines (SVMs) are supervised learning models with a wide range of applications in text classification (Joachims, 1998), image recognition (Decoste and Schölkopf, 2002), image segmentation (Barghout, 2015), anomaly detection (Schölkopf et al., Selecting the optimal decision boundary, however, is not a straightforward process.

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MLOps and the evolution of data science

IBM Big Data Hub

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. Conduct validation in the error process—This method measures how good the guesswork was by comparing it to known examples when available.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

A naïve comparison of the exposed and unexposed groups would produce an overly optimistic measurement of the effect of the ad, since the exposed group has a higher baseline likelihood of purchasing a pickup truck. Identification We now discuss formally the statistical problem of causal inference. we drop the $i$ index.

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The 2015 Digital Marketing Rule Book. Change or Perish.

Occam's Razor

Most companies are astonishingly blasé about data and possibilities of measurement. " Sad, unimaginative measurements of their sad, unimaginative campaigns. All while constantly optimizing your portfolio via controlled experiments. I told 20 people that Nikon's site is slow and profoundly sub-optimal on mobile.

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