.

# All Blog Posts Tagged 'algolytics' (5)

### Approximation or Classification – which one to choose?

Among the many decisions you’ll have to make when building a predictive model is whether your business problem is either a classification or an approximation task. It’s an important decision because it determines which group of methods you choose to create a model: classification (decision trees, Naive Bayes) or approximation (regression tree,…

Continue

Added by Algolytics on June 2, 2021 at 1:00am — No Comments

### Understanding Machine Learning: How machines learn?

“If (there) was one thing all people took for granted, (it) was conviction that if you feed honest figures into a computer, honest figures (will) come out. Never doubted it myself till I met a computer with a sense of humor.”

― Robert A. Heinlein, The Moon is a Harsh Mistress

This post is the first in a series of articles in which we will explain what Machine Learning is. You don’t have to have formal training or…

Continue

Added by Algolytics on April 13, 2017 at 4:00am — 3 Comments

### Correlation does not imply causation

A popular phrase tossed around when we talk about statistical data is “there is correlation between variables”. However, many people wrongly consider this to be the equivalent of “there is causation between variables”. It’s important to explain the distinction: Correlation means that once we know how one variable changes we can make reasonable deductions about how other variables change There are several variants of correlation:

## 1. Positive…

Continue

Added by Algolytics on December 13, 2016 at 4:30am — 1 Comment

### Understanding machine learning #3: Confusion matrix - not all errors are equal

One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let’s assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations. We evaluate 100 bank customers and our model correctly guesses in 93 instances. That may appear to be  a good result – but is it really? Should we consider a model with 93% accuracy as adequate?

It depends. Today, we…

Continue

Added by Algolytics on November 13, 2016 at 4:30am — No Comments

### Understanding machine learning: Do we need machine learning at all?

In the previous post of our Understanding machine learning series, we presented how machines learn through multiple experiences. We also explained how, in some cases, human beings are much better at interpreting data than machines. In many tasks machines still can’t replace humans, who understand surrounding reality better and can make more accurate decisions.

Machines can be given a…

Continue

Added by Algolytics on October 13, 2016 at 4:30am — No Comments

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

1999