Objective function

As explained in Inside AI

An objective function is a function that assigns a value representing the performance or desirability of a candidate solution.

How Inside AI covers it

How deep it goes

Defines it as a solution evaluator that assigns fitness scores, without specifying a formula for the evaluator.

What you can do after

Apply a chosen objective function to score candidate solutions in a genetic algorithm.

How it connects

Connects objective functions to genetic algorithms and fitness scores used to rank candidate solutions.

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Also explained in

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  Advanced Algorithms and Data StructuresA loss function is a function that quantifies the discrepancy between model predictions and observed target values.
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  Grokking Machine LearningTraining error is a measure of a model's prediction error on the data used to train it.
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  Math and Architectures of Deep LearningA function assigning a scalar penalty to model parameters or predictions.
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  Optimization Algorithms: AI techniques for design, planning, and control problemsA loss is a scalar measure of discrepancy between a model prediction and a target that an optimization procedure seeks to reduce.
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  Bayesian Optimization in ActionAn objective function is a function whose input is selected to maximize or minimize its output.
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  Grokking AI Algorithms, Second EditionTraining loss is the loss computed on the examples used to fit a model.
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  Deep Learning with PyTorch, Second EditionLoss computed on training data during fitting.
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  Natural Language Processing in Action, Second EditionLoss is a numerical measure of discrepancy between a model's prediction and a desired target, with lower values generally indicating better agreement.
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  Deep Learning with R, Third EditionA loss function is a function that assigns a numerical measure of discrepancy between a model's prediction and the true target.
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  Machine Learning System DesignA loss is a numerical value representing the discrepancy between a machine learning model's output and a target value for an instance or collection of instances.
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  Deep Learning with Python, Third EditionA function that compares predictions with targets and returns a scalar indicating how wrong the model is for a given task.
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  Sutskever's ListTraining loss is the value of an objective function measuring a model's error on examples used for parameter training.
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  Data Without LabelsA loss function quantifies the discrepancy between predicted and actual values for evaluating a model.
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  Classic Computer Science Problems in Swift: Essential techniques for practicing programmersAn error function is a mathematical function that assigns a numerical measure to the discrepancy between predicted and expected outputs.
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  Machine Learning Algorithms in DepthTraining loss is the value of a model's loss function evaluated on examples in its training dataset.
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  Statistics Every Programmer NeedsAn objective function is a mathematical expression that assigns a value to each feasible decision and is maximized or minimized in an optimization problem.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA loss function quantifies the discrepancy between model predictions and expected target values for optimization.
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  Designing Deep Learning Systems: A guide for software engineersA function that maps hyperparameter settings and their resulting training outcomes to a scalar value to be minimized or maximized.
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  Learn Generative AI with PyTorchThe objective value minimized during training, here composed of reconstruction loss and KL divergence for the VAE.
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  Build a Text-to-Image Generator (from Scratch)A loss function is a function that quantifies the discrepancy between a model's predictions and the desired outputs.
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  Introduction to Generative AI, Second EditionA loss function is a mathematical function that quantifies prediction error or undesired behavior for optimization during model training.
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  Generative AI in ActionTraining loss is a metric measuring the error between a model’s predictions and actual outcomes on training examples.
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  LLMs in ProductionA loss function is an objective that quantifies discrepancies between a model's predictions and target outputs for optimization during training.
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  Causal AIA loss function is an objective function that quantifies the cost or error associated with model parameters or predictions.
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  Reinforcement Learning from Human FeedbackA loss function is a scalar objective that quantifies prediction or optimization error during model training.
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  Graph Neural Networks in ActionA loss function is a function that quantifies the discrepancy between a model's output and the desired target.
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  Build an AI Agent (From Scratch)A metric is a quantitative indicator that aggregates evaluation judgments into a value trackable across cases or over time.
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  Statistics Slam DunkAn objective function is the mathematical function or criterion that an optimization procedure seeks to minimize or maximize.
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  AI Agents in Action, Second EditionA product metric measures business or user outcomes associated with an agent's deployed role.

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Related concepts

• validation losscontrasts with · The value of a loss function computed on held-out validation data to assess a model’s predictions.
• mean squared errorincludes · Mean squared error is the arithmetic mean of the squared differences between corresponding predicted and target numerical values.
• simulated annealingused by · Simulated annealing is a stochastic optimization method that gradually reduces the probability of accepting worse solutions to escape local optima.
• training datasetmeasures · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
• stressequivalent to · Stress is an MDS discrepancy measure that quantifies the difference between original dissimilarities and distances in the fitted low-dimensional representation.
• cross entropy lossincludes · Cross-entropy loss measures the expected negative log probability that a predicted distribution assigns to outcomes drawn from a target distribution.

This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.