PCA vs feature selection

Feature selection

Feature selection is a machine learning technique used to choose the most useful input variables from an original dataset. The goal is not to create new variables, but to keep ...

Feature selection is a machine learning technique used to choose the most useful input variables from an original dataset. The goal is not to create new variables, but to keep Read article →

PCA (principal component analysis)

PCA, short for principal component analysis, is one of the best-known dimensionality reduction methods. It is often used with numerical data to reduce the number of variables while preserving a ...

PCA, short for principal component analysis, is one of the best-known dimensionality reduction methods. It is often used with numerical data to reduce the number of variables while preserving a Read article →

Dimensionality reduction

Dimensionality reduction is a process in which data with a large number of variables is transformed into a simpler form with fewer dimensions. The goal is not to make the ...

Dimensionality reduction is a process in which data with a large number of variables is transformed into a simpler form with fewer dimensions. The goal is not to make the Read article →

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Hi, my name is Michal Krčmář. I am digital entrepreneur currently based in Prague (Czechia). I’ve been working on websites and delivering online marketing campaigns as a commercially-focused freelancer since 2006 for big prestigious brands, worked in-house for many years as consultant, manager or director and run several own startups. I founded/co-founded several companies with annual turnaround around 10+ mio USD. When I am not actually working on my business I try to constantly pass on my broad experience through seminars and online training, I also organize company trainings and workshops.

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