figure - roc curve for our logistic regression model considering our model has an accuracy and score of around %figure - makes sense where we see near perfect roc curvecheck out to see multi-class classifier roc curve in actionevaluating clustering models we discussed some of the popular ways to evaluate classification models in the previous section the confusion matrix alone provided us with bunch of metrics that we can use to compare classification models the tables are turned drastically when it comes to evaluating clustering (or unsupervised models in generalthis difficulty arises from the lack of validated ground truth in case of unsupervised modelsi the absence of true labels in the data in this sectionyou learn about some of the methods/metrics we can use to evaluate the performance of our clustering models to illustrate the evaluation metrics with real-world examplewe will leverage the breast cancer dataset available in the variables for the data and for the observation labels we will also use the -means algorithm to fit two models on this data--one with two clusters and the second one with five clusters--and then evaluate their performance km kmeans(n_clusters= random_state= fit(xkm _labels km labels_ km kmeans(n_clusters= random_state= fit(xkm _labels km labels_ external validation external validation means validating the clustering model when we have some ground truth available as labeled data the presence of external labels reduces most of the complexity of model evaluation as the clustering (unsupervisedmodel can be validated in similar fashion to classification models recall the breast cancer dataset example that we took in the first section of this we ran the labeled data through clustering algorithm in that case we had two classes and we got two clusters from our algorithm however evaluating the performance is not as straightforward as classification algorithms