How Feature Extraction Can Be Improved With Denoising

The life cycle of a machine learning models involves a training phase, where a typical data scientist develops a model with good prediction based on historical data and features extracted from the data at hand. This model is then put…

Curse Of Dimensionality And What Beginners Should Do To Overcome It

The Curse of Dimensionality is termed by mathematician R. Bellman in his book “Dynamic Programming” in 1957. According to him, the curse of dimensionality is the problem caused by the exponential increase in volume associated with adding extra dimensions to…

Implementing PCA In R With MachineHack’s How To Choose The Perfect Beer Hackathon

Dimensionality Reduction is an important and necessary step when we have a big data in hand with so many features. When there are so many features or columns, it is hard to understand the correlation between them. Including weak links…

A Hands-On Guide To Dimensionality Reduction

During the last decade, technology has advanced in tremendous ways where analytics and statistics have played major roles. These techniques fetch an enormous amount of dataset that is usually composed of many variables. For instance, the real world datasets for…

Why Should A Data Science Beginner Know About Eigendecomposition

Suppose there are a  string of assets whose value needs to be assessed based on the risk factors. These risk factors usually are correlated. To ensure profitability with a tolerable level of risk is the primary goal of a risk…

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