Linear Classification

Published

Nov 22, 2017

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Linear Classification

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We learnt that we can use a linear model (and possibly gradient descent) to fit a straight line to some data. To do this we minimised the mean-squared-error (often known as the optimisation/loss/cost function) between our prediction and the data.

It’s also possible to slightly change the optimisation function to fit the line to separate classes. This is called linear classification.

Regression: Dealing With Outliers

Published

Nov 17, 2017

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Regression: Dealing with Outliers

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Outliers are observations that are spurious. You can usually spot outliers visually; they are often far away from the rest of the observations.

Sometimes they are caused by a measurement error, sometimes noise and occasionally they can be observations of interest (e.g. fraud detection).

But outliers skew the estimates of the mean and standard deviation and therefore affect linear models that use error measures that assume normality (e.g. Minimum Squared Error).

Linear Regression

Published

Nov 17, 2017

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Linear Regression

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Regression is a traditional task from statistics that attempts to fit model to some input data to predict the numerical value of an output. The data is assumed to be continuous.

The goal is to be able to take a new observation and predict the output with minmal error. Some examples might be “what will next quater’s profits be?” and “how many widgets do we need to stock in order to fulfil demand?”.

Introduction to Gradient Descent

Published

Nov 17, 2017

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Introduction to Gradient Descent

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For only a few algorithms an analytical solution exists. For example, we can use the Normal Equation to solve a linear regression problem directly.

However, for most algorithms we rely cannot solve the problem analytically; usually because it’s impossible to solve the equation. So instead we have to try something else.

Why Correlating Data is Bad and What to do About it

Published

Nov 16, 2017

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Correlating Data

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Correlations between features are bad because you are effectively telling the model that this information is twice more important than everything else. You’re feeding the model the same data twice.

Technically it’s known as multicollinear, which is the generalisation to any number of features that could be correlated.

Generally correlating features will decrease the performance of your model, so we need to find them and remove them.

Introduction to Python and Jupyter Notebooks

Published

Nov 16, 2017

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Introduction to Python and Jupyter Notebooks

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This workshop is a quick introduction to using Python and Jupyter Notebooks.

Python

For most Data Science tasks there are two competing Open Source languages. R is favoured more by those with a mathematical background. Python is preferred by those with a programming background; all of my workshops are currently in Python.

Information and Entropy

Published

Nov 16, 2017

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Information and Entropy

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Remember the goal of data science. The goal is to make a decision based upon some data. The quality of that decision depends on our information. If we have good, clear information then we can make well informed decisions. If we have bad, messy data then our decisions will be poor.

Histograms and Skewed Data

Published

Nov 16, 2017

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Histograms and Inverting Skewed Data

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When we first receive some data, it can be in a mess. If we tried to force that data into a model it is more than likely that the results will be useless.

So we need to spend a significant amount of time cleaning the data. This workshop is all about bad data.

Entropy Based Feature Selection

Published

Nov 16, 2017

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Entropy Based Feature Selection

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One simple way to evaluate the importance of features (something we will deal with later) is to calculate the entropy for prospective splits.

In this example, we will look at a real dataset called the “mushroom dataset”. It is a large collection of data about poisonous and edible mushrooms.

Data Cleaning Example - Loan Data

Published

Nov 16, 2017

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Data Cleaning Example - Loan Data

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A huge amount of time is spent cleaning, removing, scaling data. All in an effort to squeeze a bit more performance out of the model.

The data we are using is from Kaggle, and is available in raw from from here. You will need to sign into kaggle if you want to download the full data. I’ve included just a small sample.