Understanding the problem
“the five-whys”
Different questions dramatically effect the tools and techniques used to solve the problem.
By Kenneth Jensen CC BY-SA 3.0, via Wikimedia Commons
Read moreSoftware 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/
US Supermarket Giants
Target: Optimising Marketing using customer spending data.
Walmart: Predicting demand ahead of a natural disaster.
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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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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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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Being able to evaluate models numerically is really important for optimisation tasks. However, performing a visual evaluation provides two main benefits:
It is so easy to miss a gross error when looking at summary statistics alone. Always visualise your data/results!
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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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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.
For more intuition about Bayes Rule, make sure you check out the training.
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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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In the previous workshop we plotted the decision boundary for under and overfitting classifiers. This is great, but very often it is impossible to visualise the data, usually because there are too many dimensions in the dataset.
In thise case we need to visualise performance in another way. One way to do this is to produce a validation curve. This is a brute force approach that repeatedly scores the performanc of a model on holdout data for each parameter that you specify.
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Nearest neighbour algorithms are a class of algorithms that use some measure of similarity. They rely on the premise that observations which are close to each other (when comparing all of the features) are similar to each other.
Making this assumption, we can do some interesting things like:
But more crucially, they provide an insight into the character of the data.
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