Do we really know everything about K-Means?
Clustering is not new to the Machine Learning neighborhood, but it will definitely never get old. Grouping data points with similar traits into clusters without knowing their corresponding labels, or any other prior information for that matter, sounds pretty cool to me. Let’s dive deeper into one of the most fundamental and most used algorithms that falls under the hood of Clustering; K-means.
Table of Contents
K-means
The algorithm aims to partition the data points into k sets (i.e. the clusters) by minimizing the set variances. …
THE DEFINITIVE GUIDE
3 Embedded-based methods to choose relevant features
Table of contents
This post is the third and last part of a blog series on Feature Selection. Have a look at Filter (part1) and Wrapper (part2) Methods.
Embedded Methods
Embedded methods combines the advantageous aspects of both Filter and Wrapper methods. If you take a closer look into the three different methods, you may end up wondering what is the core difference between Wrapper and Embedded methods.
At first glance, both are selecting features based on the learning procedure of the Machine Learning model. However, Wrapper methods consider unimportant features iteratively based on the evaluation metric, while Embedded…
THE DEFINITIVE GUIDE
5 Wrapper-based methods to choose relevant features
Table of contents
This post is the second part of a blog series on Feature Selection. Have a look at Filter (part1) and Embedded (part3) Methods.
Wrapper Methods
THE DEFINITIVE GUIDE
4 Filter-based methods to choose relevant features
Table of Contents
- What, When & Why
- Filter Methods
- Pearson’s Correlation
- LDA
- ANOVA
- Chi squared - What about non-linear relationships?
- PCA is not Feature Selection
- Feature Selection #NOT
Feature Selection is a very popular question during interviews; regardless of the ML domain. This post is part of a blog series on Feature Selection. Have a look at Wrapper (part2) and Embedded (part3) Methods.
What, When & Why
Are you familiar with the Iris flower data set? Isn’t it amazing how wonderful results you can have with even the simplest algorithms that exist out there?
A guide for choosing the best among a collection of algorithms (Theory & Code).
Let’s imagine that you have to buy a car. The first thing would be to look at your options; I guess they will be more than one. After thorough thinking, you probably create a list of 5 cars. What is the next step? How would you choose which one to buy? You want to be careful here, you will use it for years. I think we all know the answer… it is time for a test-drive! This is exactly what happens when you have a task in Machine Learning (ML) and a couple of algorithms that could possibly do the…
Elli Tzini
ML Engineer with a passion for NLP
