Keeping It Classy

ABSTRACT

Classification is one of the core workloads in machine learning and a natural starting point for budding data scientists. Humans have a natural inclination to classify things--that cloud looks like a tree!--but when we apply this to computers, we need to apply a bit of rigor. This talk supplies that bit of rigor, covering the necessary background to solving a classification problem. We will define key terminology, review some of the most popular and effective classification algorithms available today, and explain the aptly-named confusion matrix along the way. Examples will be primarily in Python, although no prior knowledge of the language will be necessary for this session.

ADDITIONAL MEDIA

I have put together a YouTube playlist covering the topics in this talk.

SLIDES

Click here to access the slides for this presentation.

The slides are licensed under Creative Commons Attribution-ShareAlike.

DEMO CODE

Click here to access demo code for this presentation.

The source code is licensed under the terms offered by the GPL.

LINKS & FURTHER INFO

Helpful Resources

• A review of common classification algorithms
• Another review of common classification algorithms
• Yet another review of common classification algorithms
• A tutorial of the scikit-learn classifiers
• The CART algorithm
• Another take on the CART algorithm
• The random forest ensembling algorithm
• An explanation of how bagging (bootstrap aggregating) works
• How XGBoost differs from gradient boosting in scikit-learn
• The K-Nearest Neighbors algorithm
• An overview of Passive-Aggressive classifiers
• The original Passive-Aggressive algorithms paper
• Passive-Aggressive algorithms
• Class imbalance in Passive-Aggressive algorithms
• Understanding the confusion matrix
• Classification model evaluation