Task Types
Regression, classification, ranking, clustering, dimensionality reduction and anomaly detection — what each one outputs, and why a visually separated cluster is not a business segment.
The output is a number. The loss decides which errors that number is allowed to make, and the business rarely agrees with squared error about which errors are expensive.
The model outputs a probability; the product needs a decision. The threshold between them is where the business cost lives, and it is the part that gets defaulted to 0.5.
The output is an order, judged by what sits at the top. The label is usually a click, which was produced by the previous ranking — so the model learns the old order as much as relevance.
k-means and hierarchical clustering find groups under a distance you chose. A visually separated cluster is a fact about the geometry, not about the business — until something external says otherwise.
PCA keeps variance; UMAP and t-SNE keep neighbourhoods, approximately. Neither keeps meaning, and a 2D picture of a 300-dimensional space is a drawing, not a map.
The model ranks how unusual each point is. Unusual is not the same as bad, positives are rare, and someone has to read the top of the list — so precision there is the whole product.