Evidence, Probabilities and Naive Bayes

Published: Dec 21, 2017
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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.

Published: Dec 21, 2017
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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:

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

Published: Dec 20, 2017
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Visualising Underfitting and Overfitting in High Dimensional Data

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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.

Published: Dec 20, 2017
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Nearest Neighbour Algorithms

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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:

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But more crucially, they provide an insight into the character of the data.

Published: Dec 20, 2017
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K-NN For Classification

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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)

Published: Dec 4, 2017
Author: Dr. Phil Winder
CEO

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.

Published: Nov 30, 2017
Author: Dr. Phil Winder
CEO

What 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.

Published: Nov 26, 2017
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Underfitting and Overfitting

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Imagine you had developed a model that predicts some output. The goal of any model is to generate a correct prediction and avoid incorrect predictions. But how can we be sure that predictions are as good as they can possibly be?

Now constrain your imagining to a classification task (other tasks have similar properties but I find classification easiest to reason about). We use some data to train the model. The result of the training process will be a decision boundary. I.e. class A on one side, class B on another.

Published: Nov 24, 2017
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Support Vector Machines

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If you remember from the video training, SVMs are classifiers that attemt to maximise the separation between classes, no matter what the distribution of the data. This means that they can sometimes fit noise more than they fit the data.

But because they are aiming to separate classes, they do a really good job at optimising for accuracy. Let’s investigate this below.

Published: Nov 22, 2017
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Logistic Regression

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I find the name logistic regression annoying. We don’t normally use logistic regression for anything other than classification; but statistics coined the name long ago.

Despite the name, logistic regression is incredibly useful. Instead of optimising the error of the distance like we did in standard linear regression, we can frame the problem probabilistically. Logistic regression attempts to separate classes based upon the probability that an observation belongs to a class.

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