Machine learning: What is dimensionality reduction? | Machine learning - TechTalks

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Ben Dickson, software engineer and the founder of TechTalks explains, Machine learning algorithms have gained fame for being able to ferret out relevant information from datasets with many features, such as tables with dozens of rows and images with millions of pixels. 

Photo: TechTalks

Thanks to advances in cloud computing, you can often run very large machine learning models without noticing how much computational power works behind the scenes.

But every new feature that you add to your problem adds to its complexity, making it harder to solve it with machine learning algorithms. Data scientists use dimensionality reduction, a set of techniques that remove excessive and irrelevant features from their machine learning models.

Dimensionality reduction slashes the costs of machine learning and sometimes makes it possible to solve complicated problems with simpler models...

Dimensionality reduction in the machine learning toolbox

Having too many features will make your model inefficient. But cutting removing too many features will not help either. Dimensionality reduction is one among many tools data scientists can use to make better machine learning models. And as with every tool, they must be used with caution and care.

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Source: TechTalks