Machine Learning

Industrial insight and articles from Winder.AI, focusing on the topic Machine Learning

202: Segmentation For Classification

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Segmentation

So let’s walk through a very visual, intuitive example to help describe what all data science algorithms are trying to do.

This will seem quite complicated if you’ve never done anything like this before. That’s ok!

I want to do this to show you that all algorithms that you’ve every heard of have some very basic assumption of what they are trying to do.

At the end of this, we will have completely derived one very important type of classifier.

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201: Basics and Terminology

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The ultimate goal

First lets discuss what the goal is. What is the goal?

  • The goal is to make a decision or a prediction

Based upon what?

  • Information

How can we improve the quality of the decision or prediction?

  • The quality of the solution is defined by the certainty represented by the information.

Think about this for a moment. It’s a key insight. Think about your projects. Your research. The decisions you make. They are all based upon some information. And you can make better decisions when you have more good quality information.

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102: How to do a Data Science Project

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Problems in Data Science

  • Understanding the problem

  • “the five-whys”

  • Different questions dramatically effect the tools and techniques used to solve the problem.

Data Science as a Process

  • More Science than Engineering
Research Problem Model
  • High risk
  • High reward
  • Difficult
  • Unpredictable
CRISP-DM Process

By Kenneth Jensen CC BY-SA 3.0, via Wikimedia Commons

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101: Why Data Science?

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What is Data Science?

  • Software Engineering, Maths, Automation, Data

  • A.k.a: Machine Learning, AI, Big Data, etc.

  • It’s current rise in popularity is due to more data and more computing power.

For more information: https://winderresearch.com/what-is-data-science/

Examples

US Supermarket Giants

  • Target: Optimising Marketing using customer spending data.

  • Walmart: Predicting demand ahead of a natural disaster.

Discovery

  • Most projects are “Discovery Projects”.

  • Primary Business goals: Increase Revenue, save costs, save time.

  • Budgets can come from other parts of the business.

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Testing Model Robustness with Jitter

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Testing Model Robustness with Jitter

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To test whether your models are robust to changes, one simple test is to add some noise to the test data. When we alter the magnitude of the noise, we can infer how well the model will perform with new data and different sources of noise.

In this example we’re going to add some random, normally-distributed noise, but it doesn’t have to be normally distributed! Maybe you could add some bias, or add some other type of trend!

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Quantitative Model Evaluation

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Quantitative Model Evaluation

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We need to be able to compare models for a range of tasks. The most common use case is to decide whether changes to your model improve performance. Typically we want to visualise this, and we will in another workshop, but first we need to establish some quantitative measures of performance.

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Qualitative Model Evaluation - Visualising Performance

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Qualitative Model Evaluation - Visualising Performance

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Being able to evaluate models numerically is really important for optimisation tasks. However, performing a visual evaluation provides two main benefits:

  • Easier to spot mistakes
  • Easier to explain to other people

It is so easy to miss a gross error when looking at summary statistics alone. Always visualise your data/results!

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Hierarchical Clustering - Agglomerative

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Hierarchical Clustering - Agglomerative Clustering

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Clustering is an unsupervised task. In other words, we don’t have any labels or targets. This is common when you receive questions like “what can we do with this data?” or “can you tell me the characteristics of this data?”.

There are quite a few different ways of performing clustering, but one way is to form clusters hierarchically. You can form a hierarchy in two ways: start from the top and split, or start from the bottom and merge.

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Evidence, Probabilities and Naive Bayes

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Evidence, Probabilities and Naive Bayes

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Bayes rule is one of the most useful parts of statistics. It allows us to estimate probabilities that would otherwise be impossible.

In this worksheet we look at bayes at a basic level, then try a naive classifier.

Bayes Rule

For more intuition about Bayes Rule, make sure you check out the training.

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Detrending Seasonal Data

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Detrending Seasonal Data

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statsmodels is a comprehensive library for time series data analysis. And it has a really neat set of functions to detrend data. So if you see that your features have any trends that are time-dependent, then give this a try.

It’s essentially fitting the multiplicative model:

$y(t) = Level * Trend * Seasonality * Noise$

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