Jean Franck Loa Rojas

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...

From Raw Columns to Valid Signals: What Data Preparation and Feature Engineering Actually Change

Data preparation and feature engineering are often presented as preliminary operations performed before modeling. This description understates their scientific role. Every transformation changes the representation available to the estimator and therefore changes the hypothesis th...