It’s sometimes difficult, especially for newcomers, to understand all the steps of a machine learning (ML) analysis. Here’s a practical list of the items I’m myself considering when leading an ML research project. Our use of ML is applied; we do research related to social computing (also known as computational social sciences), marketing, analytics, and […]
Tag: machine learning
Tips on Data Imputation From Machine Learning Experts
Missing values are a critical issue in statistics and machine learning (which is “advanced statistics”). Data imputation deals with ways to fill those missing values. Andriy Burkov made this statement a few days ago [1]: “The best way to fill a missing value of an attribute is to build a classifier (if the attribute is […]
Machine learning and Facebook Ads
Introduction One important thing in machine learning is feature engineering (selection & extraction). This means choosing the right variables that improve the model’s performance, while discarding those reducing it. The more impact your variables have on the performance metric, the better. Because the real world is complex, you may start with dozens or even hundreds […]
Experimenting with IBM Watson Personality Insights: How accurate is it?
Introduction I ran an analysis with IBM Watson Personality Insights. It retrieved my tweets and analyzed their text content to describe me as a person. Doing so is easy – try it here: https://personality-insights-livedemo.mybluemix.net/ I’ll briefly discuss the accuracy of the findings in this post. TL;DR: The accuracy of IBM Watson is a split decision […]
The black sheep problem in machine learning
Just a picture of a black sheep. Introduction. Hal Daumé III wrote an interesting blog post about language bias and the black sheep problem. In the post, he defines the problem as follows: The “black sheep problem” is that if you were to try to guess what color most sheep were by looking and language […]
How to teach machines common sense? Solutions for ambiguity problem in artificial intelligence
Introduction The ambiguity problem illustrated: User: “Siri, call me an ambulance!” Siri: “Okay, I will call you ‘an ambulance’.” You’ll never reach the hospital, and end up bleeding to death. Solutions Two potential solutions: A. machine builds general knowledge (“common sense”) B. machine identifies ambiguity & asks for clarification from humans The whole “common sense” […]
“Please explain Support Vector Machines (SVM) like I am a 5 year old.”
“Please explain Support Vector Machines (SVM) like I am a 5 year old.” #analytics #machinelearning #modeling Courtesy of @copperking at Reddit: https://www.reddit.com/r/MachineLearning/comments/15zrpp/please_explain_support_vector_machines_svm_like_i Direct quotation from Reddit: “We have 2 colors of balls on the table that we want to separate. We get a stick and put it on the table, this works pretty well right? […]
A.I. – the next industrial revolution?
Introduction Many workers are concerned about “robotization” and “automatization” taking away their jobs. Also the media has been writing actively about this topic lately, as can be seen in publications such as New York Times and Forbes. New York Times: After Jobs Dry Up, What Then? Forbes: Now, Even Artificial Intelligence Gurus Fret That AI […]