A modelek nem felügyelik a modeleket, és nem használják a datát a labeléd outputokkal. Értékelik, hogy a teljesítmény megfelel-e a specific metrics that measure how well the models capture underlying data structure. Tiss article discistes common metrics, their calculations, and how to intereaste results.

Common Evaluation Metrics

Severál metrics are used to asses unconfireded models, including clostering quality measures and d dimensionality reduction assessments. These metrics help how efficively the models assupressed data patterns.

Metrics és a számítások

One widely used metric i the Silhouette Score, which measures how similar an object it to to it s own cluster compared to o otheurs clusters. It ranges from -1 to 1, with higher valieres indicating better clustering.

Another metric i the Davies -Bouldin Index, which evaluates closter separation and d compactnes. Lower value supples t better clustering quality.

A For dimensionality reduction, the Exastyed Variance Ratio indicates how much informatioon i restained by principali provinents. It it is calculated by summing the variance exacained by each regulent.

Értelmezési adatok

Higher Silhouette Scores imply well-defined clusters, while e lower Davies -Bouldin Index value es suggested clar separation. In dimensionality reduction, a higher Exclayedd Variance Ratio indicates more effective data compression.

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