An introduction to logistic regression

Variables can be described as either quantitative or qualitative.
Quantitative variables have a numerical value, e.g. a person’s income, or the price of a house.
Qualitative variables have a values taken from one of different classes or categories. E.g., a person’s gender (male or female), the type of house purchased (villa, flat, penthouse, …) the colour of the eye (brown, blue, green) or a cancer diagnosis.

Linear regression predicts a continuous variable but sometime we want to predict a categorical variable, i.e. a variable with a small number of possible discrete outcomes, usually unordered (there is no order among the outcomes).

This kind of problems are called Classification.

Classification

Given a feature vector X and a qualitative response y taking values from one fixed set, the classification task is to build a function f(X) that takes as input the feature vector X and predicts its value for y.
Often we are interested also (or even more) in estimating the probabilities that X belongs to each category in C.
For example, it is more valuable to have the probability that an insurance claim is fraudulent, than if a classification is fraudulent or not.

There are many possible classification techniques, or classifiers, available to predict a qualitative response.

We will se now one called logistic regression.

Note: this post is part of a series about Machine Learning with Python.
Continue reading “An introduction to logistic regression”

Google and Microsoft blend AI into core products

I recently watched parts of both Google and Microsoft developer conferences (respectively Build 2017 and I/O 2017).
As expected, there was big emphasis on Artificial Intelligence but, in all, I liked more the Microsoft’s one while the Google’s felt too heterogeneous and without real meat (the new capabilities from Google Lens have been available e.g. at Baidu since years).

A few things that attracted my curiosity:

Vision plus X is the killer app of AI

At Google I/O, Dr. Fei-Fei Li – the new Chief Scientist of AI/ML at Google Cloud – articulated the most convincing vision: Continue reading “Google and Microsoft blend AI into core products”

Machines “think” differently but it’s not a problem (maybe)

Yet another article about the interpretability problem of many AI algorithms, this time on the MIT Technology Review, May/June 2017 issue.

The issue is clear; many of the most successful recent AI technologies revolve around deep learning: complex artificial neural networks – with so many layers of so many neurons transforming so many variables – that behave like “black boxes” for us.
We cannot comprehend anymore the model, we don’t know how or why the outcome to a specific input is obtained.
Is it scary?

In the film Dekalog 1 by Krzysztof Kieślowski – the first of ten short films inspired to the ten Christian imperatives, the first one being “I am the Lord your God; you shall have no other gods before me”  – Krzysztof lives alone with Paweł, his 12-years-old and highly intelligent son, and introduces him to the world of personal computers. Continue reading “Machines “think” differently but it’s not a problem (maybe)”