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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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In a previous workshop we investigated how the nearest neighbour algorithm uses the concept of distance as a similarity measure.
We can also use this concept of similarity as a classification metric. I.e. new observations will be classified the same as its neighbours.
This is accomplished by finding the most similar observations and setting the predicted classification as some combination of the k-nearest neighbours. (e.g. the most common)
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https://prometheus.io is an open source time series database that focuses on capturing measurements and exposing them via an API. I love Prometheus because it it so simple; it’s minimalism is its greatest feature. It achieves this by pulling metrics from instrumented applications, not pulling like many of its competitors. In other words Prometheus “scrapes” the metrics from the application.
This means that it works very well in a distributed, cloud-native environment. All of the services are unburdened by load on the monitoring system. This has knock on effects meaning that HA is supported through simple duplication and scaling is supported through segmentation.
Read moreWhat do you mean by monitoring? Why do you need it? What are the real needs and are you monitoring them? Ask yourself these questions. Can you answer them? If not, you’re probably doing monitoring wrong.
This post asks the basic question. What is monitoring? How does it compare to logging and tracing? Let’s find out.
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