Peruvian University of Applied Sciences

There Are No Patterns Before a Representation: What Unsupervised Learning Actually Sees

Unsupervised learning is often described as discovering hidden structure in unlabeled data. That description omits the analytical object that makes discovery possible: a representation, a scale, a dissimilarity rule, and a neighborhood definition. Before an algorithm can identify...

There Are No Patterns Before a Representation: What Unsupervised Learning Actually Sees

Unsupervised learning is often described as discovering hidden structure in unlabeled data. That description omits the analytical object that makes discovery possible: a representation, a scale, a dissimilarity rule, and a neighborhood definition. Before an algorithm can identify...

The Curve Is an Average of Worlds: What Partial Dependence Actually Shows

Partial dependence plots are often interpreted as direct response curves: set a feature to a value, average the predictions, and read the resulting line as an explanation. This paper evaluates a narrower object. A marginal partial-dependence curve averages a frozen predictive mod...

The Curve Is an Average of Worlds: What Partial Dependence Actually Shows

Partial dependence plots are often interpreted as direct response curves: set a feature to a value, average the predictions, and read the resulting line as an explanation. This paper evaluates a narrower object. A marginal partial-dependence curve averages a frozen predictive mod...

Each Tree Corrects the Last: What Gradient Boosting Actually Learns

Gradient boosting is commonly described as a sequence of weak trees in which each new learner corrects errors left by the current ensemble. This description is computationally accurate but scientifically incomplete. Every correction is conditioned on a declared loss function, an...

Each Tree Corrects the Last: What Gradient Boosting Actually Learns

Gradient boosting is commonly described as a sequence of weak trees in which each new learner corrects errors left by the current ensemble. This description is computationally accurate but scientifically incomplete. Every correction is conditioned on a declared loss function, an...

The Metric Chooses the Winner: What Model Evaluation Actually Rewards

A model is not inherently “best” independently of the rule used to evaluate it. Evaluation metrics determine which errors receive greater weight, which properties of a prediction system become visible, and which model appears superior. Consequently, model selection is incomplete...

The Path to the Minimum Matters: What an Optimizer Actually Changes

An optimizer does not ordinarily redefine the statistical target encoded by a loss function, but it determines which solution is reached, how closely it is approached, how stable that approach is, and how much computational work is required. Therefore, a valid objective does not...

The Metric Chooses the Winner: What Model Evaluation Actually Rewards

A model is not inherently “best” independently of the rule used to evaluate it. Evaluation metrics determine which errors receive greater weight, which properties of a prediction system become visible, and which model appears superior. Consequently, model selection is incomplete...

The Path to the Minimum Matters: What an Optimizer Actually Changes

An optimizer does not ordinarily redefine the statistical target encoded by a loss function, but it determines which solution is reached, how closely it is approached, how stable that approach is, and how much computational work is required. Therefore, a valid objective does not...