Month: January 2017

Multi-label Classification: A Guided Tour

Introduction

I recently undertook some work that looked at tagging academic papers with one or more labels based on a training set.

A preliminary look through the data revealed about 8000 examples, 2750 features, and…650 labels. For clarification, that’s 2750 sparse binary features (keyword indices for the articles), and 650 labels, not classes. Label cardinality (average number of labels per example) is about 2, with the majority of labels only occurring a few times in the dataset…doesn’t look good, does it? Nevertheless, more data wasn’t available and label reduction wasn’t on the table yet, so I spent a good amount of time in the corners of academia looking at multi-label work. 

Continue reading “Multi-label Classification: A Guided Tour”

Income Analysis – US Census Data

A couple months back, I worked on analysis and predictive modeling of US salary given census data. Full Jupyter notebook here, below are some details and some of the more interesting findings.

In general, metadata is below and contains lots of null values (as you might suspect of census data).

screen-shot-2017-01-22-at-11-21-22-pm Continue reading “Income Analysis – US Census Data”

What Goes First – Speed or Strength?

I recently had access to a lot of baseball data, specifically data on every season of every player in the history of the MLB going back to 1871. Here’s some analysis on how baseball players lose speed and strength (or both) throughout their career. Analysis primarily consisted of variable creation and data queries. Unfortunately, code not available 😦

Continue reading “What Goes First – Speed or Strength?”

Trump Tweet Analysis

This project stems from two overarching questions:

Which emotions do politicians most frequently appeal to?

I recently saw a BuzzFeed presentation on, among other things, the virality of BuzzFeed content. A big part of their business relies on understanding what kind of content goes viral and why, so their data science team understandably spends a lot of time not only looking at how a piece of content becomes widely popular, but also looking at the distribution of content types in their most popular pieces of content. Continue reading “Trump Tweet Analysis”

Article Classification and News Headlines Over Time

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How does front page news track a single topic over a period of time? What’s the media’s attention span for a given story?

In general, many find it surprising how quickly major media outlets shift their attention from one story to another. This is partly a reflection of our own attention spans and appetites, and is partly due to the fact that media organizations are incentivized to be the first to break news; as a result readers are more likely to be bombarded with what’s novel instead of what’s important. Continue reading “Article Classification and News Headlines Over Time”

Building a Recurrent Neural Network to Generate Novel Text

Introduction

The purpose of this quick tutorial is to get you a very big, very useful neural network up and running in just a few hours. The goal is that anyone with a computer, some free time, and little-to-no knowledge of what neural networks are or how they work can easily begin playing with this technology as soon as possible. Technical explanations of what RNNs are abound on the internet, so this tutorial will skip explanation and focus solely on building. Continue reading “Building a Recurrent Neural Network to Generate Novel Text”

Introduction to Regularization

What is regularization? Regularization, as it is commonly used in machine learning, is an attempt to correct for model overfitting by introducing additional information to the cost function. In this post we will review the logic and implementation of regression and discuss a few of the most widespread forms: ridge, lasso, and elastic net. For simplicity, we’ll discuss regularization within the context of least squares linear regression, and I assume that you have some familiarity with linear regression. Onward! Continue reading “Introduction to Regularization”

Short Introduction to PCA

In Principal Component Analysis (PCA), we would like to convert our high-dimensional dataset onto a lower-dimensional space while keeping as much information as possible. Typically, this is done to avoid curse of dimensionality effects or for the purposes of data visualization.

In broad strokes, PCA reduces the dimensionality of our dataset in a way that minimizes (certain aspects of) the amount of information we throw away by projecting our p-dimensional feature set onto a lower-dimensional subspace. Continue reading “Short Introduction to PCA”