Model Evaluation

Artificial Intelligence / 07 Sep 2026 / 13 min read

Handle Label Shift in Deployed Classifiers

A classifier can keep seeing familiar inputs and still make worse decisions after deployment. One reason is that the frequency of the classes has changed. Imagine a model trained to classify support tickets as billing, account, or technical. During training, billing tickets made up 20% of examples. After a pricing migration, billing issues temporarily rise to 50%. The model has not changed, but one part of the environment has: the prior probability of each class.

Artificial Intelligence Machine Learning

Artificial Intelligence / 07 Sep 2026 / 9 min read

Defer Uncertain Classifier Predictions with Selective Classification

A classifier does not have to make an automated decision for every input. In many systems, forcing a prediction on the hardest cases is exactly what creates expensive mistakes. Imagine a model that routes customer support messages. Clear password-reset requests can be handled automatically, while ambiguous messages could be sent to a human queue. The important design question is no longer only “How accurate is the classifier?” It is also “How accurate is it on the cases we allow it to handle?”

Artificial Intelligence Machine Learning

Artificial Intelligence / 06 Sep 2026 / 10 min read

Use Best-of-N Sampling to Spend More Compute at Inference

A language model can produce several plausible answers to the same prompt. That variability is often treated as noise, but it can also be used deliberately: generate multiple candidates, evaluate them, and return the strongest one. This pattern is called best-of-N sampling. Instead of trusting one generation, the system samples N responses and uses a scoring rule to select one. The extra samples spend more compute at inference time in exchange for more opportunities to find a good response.

Artificial Intelligence LLM

Artificial Intelligence / 06 Sep 2026 / 9 min read

Inspect Language Model Uncertainty with Token Entropy

A language model can produce fluent text even when several continuations look similarly plausible to the model. Looking only at the selected token hides that ambiguity: a token chosen with probability 0.90 and one chosen from a nearly even 0.51 versus 0.49 split both appear as a single output token. Token entropy summarizes how spread out the model’s next-token probability distribution is. It can help developers inspect uncertain generation steps, compare decoding behavior under controlled conditions, and build diagnostic signals for evaluation. But entropy is not a probability that the model is correct, and using it as one leads to unreliable decisions.

Artificial Intelligence Large Language Models

Artificial Intelligence / 06 Sep 2026 / 10 min read

Compare Model Distributions with KL Divergence

AI systems often produce probability distributions rather than single answers. A classifier assigns probabilities to classes, a language model assigns probabilities to possible next tokens, and a teacher model can provide a soft target distribution for a smaller student. In all of these cases, developers need a way to ask: how different is one probability distribution from another? Kullback-Leibler divergence, usually shortened to KL divergence, is one answer. It measures how much a comparison distribution Q differs from a reference distribution P, with the differences weighted by what P considers important.

Artificial Intelligence Machine Learning

Artificial Intelligence / 05 Sep 2026 / 12 min read

Split Conformal Classification for Prediction Sets

A classifier usually returns one label or a vector of scores. That is convenient when the application must choose one answer, but it hides an important distinction: some inputs strongly support one class, while others leave several classes plausible. Conformal prediction provides a way to expose that ambiguity. For classification, it can return a prediction set containing one or more labels instead of forcing every input into a single choice. With an appropriate calibration procedure and statistical assumptions, the method can target a long-run coverage level such as 90%: roughly speaking, the true label should appear in the prediction set for at least that proportion of future examples.

Artificial Intelligence Machine Learning

Artificial Intelligence / 05 Sep 2026 / 9 min read

Prevent Target Leakage in Machine Learning Evaluation

A machine learning model can score extremely well in offline evaluation and fail as soon as it reaches production. Sometimes the model is not the main problem. The evaluation accidentally gave it information that would not exist when a real prediction is made. This failure is called target leakage: information related to the outcome enters the model inputs in a way that makes the target easier to predict than it will be at inference time. Leakage can produce impressive metrics because the model is solving an easier, unrealistic problem.

Artificial Intelligence Machine Learning

Artificial Intelligence / 05 Sep 2026 / 10 min read

Model Ensembling for Combining Predictions

A machine learning model can fail because of patterns specific to its training run: its initialization, sampled batches, training data, architecture, or hyperparameters. Training another model may produce different mistakes. Ensembling uses that disagreement by combining predictions from multiple models instead of trusting one model alone. The idea is simple, but useful ensembles require more than averaging everything available. Models that make nearly identical errors provide little complementary information, while diverse models can improve predictions at the cost of additional training, memory, and inference work.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Stop Neural Network Training at the Right Time with Early Stopping

Training a neural network for more steps usually gives the optimizer more opportunities to reduce training loss. That does not mean the resulting model will perform better on unseen data. After useful patterns have been learned, continued training can increasingly fit details that are specific to the training set. Early stopping turns this observation into a practical training rule: evaluate the model on held-out validation data during training, remember the best checkpoint, and stop when meaningful validation improvement has not appeared for long enough.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Perplexity for Language Model Evaluation

A language model can assign high probability to likely text and low probability to unlikely text, but developers still need a compact way to summarize that behavior across many tokens. Perplexity is one common metric for this job. Perplexity is useful when comparing probabilistic language models on the same evaluation data under compatible tokenization and scoring rules. It is much less useful as a general score for whether generated answers are correct, helpful, safe, or well written.

Artificial Intelligence LLM

Artificial Intelligence / 04 Sep 2026 / 10 min read

Macro F1 and Balanced Accuracy for Imbalanced Classifiers

A classifier can report impressive accuracy while failing on the cases you care about most. This happens easily when one class is much more common than another. Imagine a model that detects defective components on a production line. In a test set of 1,000 components, 950 are normal and 50 are defective. A model that predicts normal for every component is correct 95% of the time, yet it detects none of the defects.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Interpret Language Model Perplexity Correctly

A language model can improve on its training objective while still leaving an important question unanswered: how well does it predict text it did not train on? Perplexity is a compact way to measure that predictive fit for autoregressive language models, but the number is easy to misuse. A lower perplexity can mean that a model assigns higher probability to held-out text. It does not automatically mean that the model follows instructions better, reasons more reliably, hallucinates less, or produces more useful answers. Comparisons can also become misleading when tokenization, evaluation data, or context handling differs.

Artificial Intelligence LLM

Artificial Intelligence / 04 Sep 2026 / 9 min read

Estimate Model Uncertainty with Monte Carlo Dropout

A neural network can produce a confident-looking prediction without telling you how sensitive that prediction is to uncertainty in the learned model. This matters when an application must decide whether to trust a prediction, request more information, or route a case for review. Monte Carlo dropout, often shortened to MC dropout, provides one practical uncertainty signal for networks trained with dropout. Instead of disabling dropout for inference, it keeps dropout stochastic and evaluates the same input repeatedly. Variation across those predictions reveals how strongly the result depends on the sampled dropout masks.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Detect Out-of-Distribution Inputs Before Trusting a Model

A model can produce a confident-looking prediction for an input that is unlike anything it was designed to handle. A product classifier trained on shoes, shirts, and bags still has to return some class when given a photo of a bicycle. The classifier’s output layer does not automatically gain an unknown class just because the input is unfamiliar. This is the problem addressed by out-of-distribution detection, usually shortened to OOD detection. The goal is to recognize inputs that differ meaningfully from the data the model is expected to handle, before the application treats an ordinary model prediction as trustworthy.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Detect Distribution Shift Before Model Quality Fails

A model can pass offline evaluation and still become less useful after deployment. The model may not have changed at all. Instead, the data reaching it may have changed. A fraud classifier trained on last year’s transactions may encounter a new payment pattern. A support-ticket model may see terminology introduced by a new product. An image model deployed to different hardware may receive images with different lighting or compression. These are forms of distribution shift: the statistical conditions seen in production differ from those represented by the data used to develop or evaluate the model.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Design Model Abstention for Uncertain Predictions

A model does not have to make a decision on every input. In many applications, forcing a prediction is exactly what turns an uncertain case into an expensive mistake. Consider a classifier that routes support tickets to billing, account, or technical teams. Most tickets are straightforward, but some are vague or combine several problems. If the application automatically accepts every prediction, the model must act even when its evidence is weak. A better system can automate clear cases and send uncertain ones to a fallback such as human review.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 9 min read

Stop Model Training at the Right Time with Early Stopping

Training a model for more epochs does not guarantee a better model. Training loss may keep falling while performance on unseen data stops improving or begins to degrade. Continuing from that point consumes compute and can leave you with a checkpoint that generalizes worse than an earlier one. Early stopping turns validation performance into a stopping rule. Instead of choosing a fixed number of epochs and hoping it is appropriate, you monitor a validation metric, keep the best checkpoint, and stop after the metric has failed to improve for a defined amount of time.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 8 min read

Choose Classification Thresholds with Precision and Recall

A binary classifier often produces a score rather than a final yes-or-no answer. An image model might estimate a 0.82 probability that a component is defective, while a moderation model might assign a 0.37 score to unwanted content. The classification threshold turns that continuous score into a decision. A threshold of 0.5 is common, but it is not automatically correct. The right threshold depends on which mistakes matter, how frequently the positive class occurs, and what happens after the model makes a prediction.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 10 min read

Calibrate Classifier Confidence for Better Decisions

A classifier can predict the correct label often and still produce confidence scores that are difficult to trust. Suppose a model marks 1,000 transactions as fraudulent with confidence near 0.9. If that confidence behaves like a useful probability, roughly 90% of comparable predictions should actually be fraud. If only 65% are, the model is overconfident. If nearly all are fraud, it is underconfident. This distinction matters whenever a system uses model scores to make decisions: escalating cases to humans, approving automated actions, ranking alerts, or choosing a threshold based on expected risk. Accuracy tells you how often predictions are correct. Calibration asks whether predicted probabilities match observed frequencies.

Artificial Intelligence Machine Learning

Data Science / 01 Sep 2026 / 4 min read

Time Series Cross-Validation with Walk-Forward Splits

Random train/test splits assume examples are exchangeable. Time-series data violates that assumption because the future occurs after the past, and production models normally predict observations that were not available during training. Walk-forward validation preserves that chronology. Why random splitting is misleading Suppose you want to predict next week’s demand from historical sales. A random split can place March observations in the test set while April observations appear in training. Even if features do not explicitly contain future values, the evaluation now uses a model fitted on a future regime. Seasonality, pricing, inventory, customer behavior, and economic conditions can all make the score more optimistic than deployment reality.

Data Science Machine Learning

Data Science / 01 Sep 2026 / 5 min read

Probability Calibration for Classification Models

A classifier can rank examples correctly while producing probabilities that are poor estimates of real-world likelihood. If a model assigns 0.8 probability to many comparable cases, calibration asks whether roughly 80% of those cases are actually positive. This matters whenever probabilities drive decisions such as pricing, triage, alert thresholds, expected value, or human review. Discrimination and calibration are different Metrics such as ROC AUC evaluate how well a model ranks positive examples above negative ones. They do not require predicted probabilities to match observed frequencies.

Data Science Machine Learning

Data Science / 01 Sep 2026 / 5 min read

Avoiding Data Leakage in Machine Learning Pipelines

Data leakage happens when information that would not be available at prediction time influences model training. The result is an evaluation score that looks excellent in development and collapses after deployment. Leakage is often subtle because the model code itself can be correct. The mistake lives in how datasets, features, preprocessing, and time boundaries are constructed. Split before learning from the data A classic mistake is standardizing the full dataset and splitting afterward.

Data Science Machine Learning