# fa.bianp.net

TL; DR These are some notes on calibration of surrogate loss functions in the context of machine learning. But mostly it is …

As part of the development of memory_profiler I've tried several ways to get memory usage of a program from within Python. In this post I'll describe the different alternatives I've tested.

### The psutil library

psutil is a python library that provides an interface for retrieving information on running processes. It …

In this post I compar several implementations of Logistic Regression. The task was to implement a Logistic Regression model using standard optimization …

TL;DR: I've implemented a logistic ordinal regression or proportional odds model. Here is the Python code

The logistic ordinal regression model …

My latest contribution for scikit-learn is an implementation of the isotonic regression model that I coded with Nelle Varoquaux and Alexandre Gramfort …

Householder matrices are square matrices of the form

$$P = I - \beta v v^T$$

where $\beta$ is a scalar and $v$ is …

Note: this post contains a fair amount of LaTeX, if you don't visualize the math correctly come to its original location

In …

Besides performing a line-by-line analysis of memory consumption, memory_profiler exposes some functions that allow to retrieve the memory consumption of a function in real-time, allowing e.g. to visualize the memory consumption of a given function over time.

The function to be used is memory_usage. The first argument specifies what …

SciPy contains two methods to compute the singular value decomposition (SVD) of a matrix: scipy.linalg.svd and scipy.sparse.linalg.svds. In this post I'll compare both methods for the task of computing the full SVD of a large dense matrix.

The first method, scipy.linalg.svd, is perhaps …

This tutorial introduces the concept of pairwise preference used in most ranking problems. I'll use scikit-learn and for learning and matplotlib for …