What technique is used to minimize loss for a large data set?

Using dimensionality reduction techniques, of course. You can use this concept to reduce the number of features in your dataset without having to lose much information and keep (or improve) the model’s performance. It’s a really powerful way to deal with huge datasets, as you’ll see in this article.

Which method would you choose for dimensionality reduction?

The various methods used for dimensionality reduction include: Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Generalized Discriminant Analysis (GDA)

Which type of algorithm should be used for dimensionality reduction?

Linear Discriminant Analysis, or LDA, is a multi-class classification algorithm that can be used for dimensionality reduction.

What is are the main approach es for dimensionality reduction?

The most common approach to dimensionality reduction is called principal components analysis or PCA.

Which of the following techniques can be used to handle high dimensional data?

Principal component analysis(PCA) and SOM are used to handle this situation.

What is the need of dimensionality reduction explain any two techniques for dimensionality reduction in data mining?

Dimensionality reduction technique can be defined as, “It is a way of converting the higher dimensions dataset into lesser dimensions dataset ensuring that it provides similar information.” These techniques are widely used in machine learning for obtaining a better fit predictive model while solving the classification …

Which is not data reduction Stratergy?

Discussion Forum

Which technologies are typically used for data reduction?

Data deduplication and compression technologies are common data reduction technologies aimed at improving data transfer, processing, and storage efficiency with less redundant data.

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