Rootstrap Blog

Anthony Figueroa

Total 38 Posts Website

Recommendation Systems: How Suggestion Algorithms Work

Oftentimes, we are surprised by the accuracy of recommendations on what to buy on Amazon, watch on Netflix, or listen on Spotify. We feel that somehow these companies know how our brain works and monetizing this magical guessing game. They have a deep foundation on behavioral sciences, and our job

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From Summarization to Generalization and Prediction

We can make predictions with machine learning by generalizing our data’s pertinent characteristics. Summarizing diverse datasets provides insight that can help produce more relevant generalizations.

Data predictions provide probabilities of future outcomes by mining and analyzing existing data, also called training data. Effective prediction is a mix of engineering, statistics, and intuition. Summarization can help by shaping this intuition. In the generalization phase, we test our training data against new data, called test data, to calculate if our model is good enough to be used in real life. These two processes simplify large multidimensional datasets, so machine learning predictions can be applied to them. This article describes how summarization leads to generalization and then prediction through a real estate example.

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Data Samples and error visualization techniques

Why we should choose representative samples with error in mind when we build data visualizations. A brief overview of uncertain bar charts and uncertain ranked lists.

The type of data samples that populate our visualizations can add uncertainty to our results. Some common data displays like bar and pie charts work better than others for making that uncertainty understandable. This article explores how to understand our data samples and create the most suitable graphs for visualizing what they represent.
In general, the goals of data science are to understand data and generate predictive models that help us make better decisions. For a more thorough overview of data visualization, see “Data visualization and The Truthful Art.”

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