ML101

Explaining Your Machine Learning Model (or 5 Ways to Assess Feature Importance)

Machine Learning can often be a black box. To gain actionable insights, its helpful to know how a variable influences a model. Here we outline 5 ways to assess feature importance to affecting the probability of an outcome.

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How to Automatically Segment Your Data with Clustering

One of the most common analyses we perform is to look for patterns in data. What market segments can we divide our customers into? How do we find clusters of individuals in a network of users?

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4 Reasons Your Machine Learning Model is Wrong (and How to Fix It)

A seemingly good machine learning model may still be wrong. We’ll show how you can evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall, and present some solutions for such scenarios.

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What is an Artificial Neural Network?

These days we hear a lot about Artificial Neural Networks. Leading companies, from Facebook, to Google, to Zillow use them throughout their core products. But what are Neural Networks? And when should you use one?

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How to Predict Yes/No Outcomes Using Logistic Regression

Often we want to predict discrete outcomes in our data. Can an email be designated as spam or not spam? Was a transaction fraudulent or valid?

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How to Predict Any Value Using Linear Regression

One of the most common questions we have of our data is evaluating the value of something. How many items will we sell next month? How much does it cost to produce them? How much revenue will we make over the year?

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The Two Types of Machine Learning

In this post, we review common applications of Machine Learning, and the differences between the two subtypes of Supervised vs. Unsupervised Machine Learning.

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