I asked ChatGPT to: explain double machine learning for dummies Here is the answer: Double machine learning (DML) is a statistical method used to estimate treatment effects in the presence of high-dimensional controls. To put it simply, it’s a way to answer questions about cause and effect using data and machine learning, even when there […]
Double Machine Learning for Dummies as explained by ChatGPT Read More »
Yes, this is clearly nonsense. Sorting is not a hard problem and standard algorithms such as quicksort and mergesort have O(x^2) and O(n log(n)) complexity. But let me scratch this itch of sorting numbers using an evolutionary algorithm, specifically Covariance matrix adaptation evolution strategy (CMA-ES). Technically, we will use what I think is the original
How to sort numbers with an evolutionary algorithm (CMA-ES) Read More »
This article on causal machine learning covers a practical example of how to learn structural causal models (SCM) directly from data. We will use bnlearn, which is an open-source library for learning the graphical structure of Bayesian networks in Python. Check out my Github repo for additional code examples. For other frameworks, checkout my page
How to use bnlearn to learn causal structures Read More »
Cosine similarity is the normalised dot product between two vectors. I guess it is called “cosine” similarity because the dot product is the product of Euclidean magnitudes of the two vectors and the cosine of the angle between them. If you want, read more about cosine similarity and dot products on Wikipedia. Here is how
Cosine similarity in Python Read More »
Step one: brew install glpk pip install pulp Step two: from pulp import * prob = LpProblem(“test1”, LpMinimize) # Variables x = LpVariable(“x”, 0, 4, cat=”Integer”) y = LpVariable(“y”, -1, 1, cat=”Integer”) z = LpVariable(“z”, 0, cat=”Integer”) # Objective prob += x + 4*y + 9*z # Constraints prob += x+y = 10 prob +=
(Integer) Linear Programming in Python Read More »
Whenever two things have a directional relationship to each other, then you can compute the pagerank of those things. For example, you can observe a directional relationships between web pages that link to each other, scientists that cite each other, and chess players that beat each other. The relationship is directional because it matters in
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Having reinstalled PostgreSQL with support for Python and pointing at my non-system python, it is time to test whether I can use the convex optimizer library I’ve installed in my Python 2.7 (pip install cvxopt). Install PL/Python if not already installed — if not already installed. Doesn’t hurt. create extension plpythonu; Create a function that
Running LP-solver in Postgres Read More »
In a project I’m going to use clustering algorithms implemented in Python, such as k-means. Clustering http://stackoverflow.com/questions/1545606/python-k-means-algorithm scipy.cluster has been reported to have some problems, so for now I’ll use PyCluster (following advice given on stackoverflow). Install PyCluster: pip install PyCluster
Clustering in Python Read More »
Fast compression algorithms like Snappy, QuickLZ and LZ4 are designed for a general stream of bytes, and typically don’t treat byte-sequences representing numbers in any special way. Geospatial data is special in the sense that it often contains a large amount of numbers, like floats, representing coordinates.
Is there a need for a fast compression algorithm for geospatial data? Read More »
Rtree is a ctypes Python wrapper of libspatialindex that provides a number of advanced spatial indexing features for the spatially curious Python user.
Trying a Python R-tree implementation Read More »