Category: Data Science

The Stats Guy takes a look at averages:

In this diagram, there are a bunch of numbers and a single question mark. Behind the question, is also a number. The known numbers are the same as in our friend v above.

Our task is as follows:

– Make a guess on what that mystery number could be. And,
– If we can’t get it right, then reduce, as much as possible, the error we incur on our guess.

This is a well-written explanation of an important concept. H/T R-Bloggers

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David Robinson takes us through simulation of a random walk in R:

What’s fun about this problem is that it’s an example of a random walk: a stochastic process made up of a sequence of random steps (in this case, left or right). What makes this a fun variation is that it’s a random walk in a circle- passing 5 to the left is the same as passing 15 to the right. I wasn’t previously familiar with a random walk in a circle, so I approached it through simulation to learn about its properties.

Click through for a simulation. Or 50,000 of them.

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Ram Tavva walks us through the algorithm to design decision trees:

A decision tree is made up of several nodes:

1.Root Node: A Root Node represents the entire data and the starting point of the tree. From the above example the
First Node where we are checking the first condition, whether the movie belongs to Hollywood or not that is the
Rood node from which the entire tree grows
2.Leaf Node: A Leaf Node is the end node of the tree, which can’t split into further nodes.
From the above example ‘watch movie’ and ‘Don’t watch ‘are leaf nodes.
3.Parent/Child Nodes: A Node that splits into a further node will be the parent node for the successor nodes. The
nodes which are obtained from the previous node will be child nodes for the above node.

Read on for an example of implementation in R.

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Bryan Shalloway has an interesting article for us:

In classification problems, under and over sampling techniques shift the distribution of predicted probabilities towards the minority class. If your problem requires accurate probabilities you will need to adjust your predictions in some way during post-processing (or at another step) to account for this.

Bryan has a clear example showing this problem in action.

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George Pipis explains two key concepts of a distribution:

Most commonly a distribution is described by its mean and variance which are the first and second moments respectively. Another less common measures are the skewness (third moment) and the kurtosis (fourth moment). Today, we will try to give a brief explanation of these measures and we will show how we can calculate them in R.

Click through for an explanation. H/T R-Bloggers

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Lionel Hertzog shows off spatial capabilities in R:

All of these operations follow the same logic, st_operation(A, B) checks for each combinations of the geometries in A and B whether A operation B is true or false. For instance st_within(A, B) checks whether the geometries in A are within B, this is similar to st_contains(B, A), the difference between the two being the shape of the returned object. If A has n geometries and B has m, st_contains(B, A) returns a list of length m where each elements contains the row IDs (numbers between 1 and n) of the geometries in A satisfying the operation. By using sparse=FALSE the functions returns matrices, like st_within(A, B, sparse=FALSE) returns a n x m matrix, st_within(B, A, sparse=FALSE) returns a m x n matrix. Note that running st_operation(A, A) checks the operation between all geometries of the object, so returning a n x n matrix.

Click through for part 1 of the series.

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