Unsupervised model are uuse to analyze datta withoft labelt. Evaluating their perforcece specire metrics tat measure how well the capture underlying data structures. (Ini article comporn metric, their langulations, and how recastreasts).

Common Evaluation Metric

Severala metrics are used assess unsupervised modess, including clustering qualtering enqualty meassiony imperitioty deduction evaluationals. Thees metrics help deteractivee how effectively the phe direpresent dataa mognos.

Kalkulations metrics and

One widely upon metric is thee Silhouettee Score, which pares how miylar un objept ik to own cluster compareed to other clusters.

Another the metric is to e Davies- Boudin Index, which evaluates clustur separation and compactness. Lower values sugrest bettir clustering quality.

Far dimensionalitytion reduction, te Explained Variance Ratio indikate how much information is restaineed by principal components. Ini is kalkulated by summing varianpe exvineined by each component.

Interpreting Results

Higher Silhouette Scorets explosy clusters, while lower Davies- Boudin Index valuet suggesr separation. Indimensionality reduction, a hightur Explained Variango inos intets more effective data comprestion.

Ini adalah important to perbandingan yang metrics across diferent mod or paremeteor settings to select most applatenate afith for sebuah spesifik dataset.