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Interpreting PCA Results and Explained Variance #ai #artificialintelligence #machinelearning
After implementing PCA, it's crucial to interpret the results correctly. A key concept here is the explained variance, which tells you how much information (or variance) is retained by each principal component. The 'explained_variance_ratio_' attribute of the PCA object provides the percentage of variance explained by each of the selected ...
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