Why model calibration matters and how to achieve it
Calibrated models make probabilistic predictions that match real
world probabilities. This post explains why calibration matters, and
how to achieve it. It discusses practical issues that calibrated
predictions solve and presents a flexible framework to calibrate any
classifier. Calibration applies in many applications, and hence the
practicing data scientist must understand this useful tool.
Source: Why model calibration matters and how to achieve
an article by Lee Richardson & Taylor Pospisil
Patterns of Functional Programming
One of the ideas of functional programming is having pure functions,
functions that have no side effects. But writing programs made
exclusively from functions without side effects can't be useful in
the real world, because programs have to affect the real world
somehow. Inevitably, this means some parts of our programs must be
effectful for the program to be useful.
Source: Patterns of Functional Programming: Functional Core -
an article by Javier Casas.