PCA can help us improve performance at a meager cost of model accuracy. Models running on high-dimensional data might perform very slowly or even fail, requiring significant server resources. Īlgorithm performance typically depends on the dimension of the data. This helps us deal with the “curse of dimensionality”. For example, by reducing the dimensionality of the data, PCA enables us to better generalize machine learning models. PCA is an unsupervised learning technique that offers a number of benefits. According to Wikipedia, PCA (or Principal Component Analysis) is a “statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables…into a set of values of linearly uncorrelated variables called principal components.” The Benefits of PCA (Principal Component Analysis) PCA is a dimensionality reduction framework in machine learning.
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