Overfitting and Underfitting
AblationAccuracy in Machine LearningActive Learning (Machine Learning)Adversarial Machine LearningAffective AIAI AgentsAI and EducationAI and FinanceAI and MedicineAI AssistantsAI DetectionAI EthicsAI Generated MusicAI HallucinationsAI HardwareAI in Customer ServiceAI Recommendation AlgorithmsAI RobustnessAI SafetyAI ScalabilityAI SimulationAI StandardsAI SteeringAI TransparencyAI Video GenerationAI Voice TransferApproximate Dynamic ProgrammingArtificial Super IntelligenceBackpropagationBayesian Machine LearningBias-Variance TradeoffBinary Classification AIChatbotsClustering in Machine LearningComposite AIConfirmation Bias in Machine LearningConversational AIConvolutional Neural NetworksCounterfactual Explanations in AICurse of DimensionalityData LabelingDeep LearningDeep Reinforcement LearningDifferential PrivacyDimensionality ReductionEmbedding LayerEmergent BehaviorEntropy in Machine LearningEthical AIExplainable AIF1 Score in Machine LearningF2 ScoreFeedforward Neural NetworkFine Tuning in Deep LearningGated Recurrent UnitGenerative AIGraph Neural NetworksGround Truth in Machine LearningHidden LayerHuman Augmentation with AIHyperparameter TuningIntelligent Document ProcessingLarge Language Model (LLM)Loss FunctionMachine LearningMachine Learning in Algorithmic TradingModel DriftMultimodal LearningNatural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Querying (NLQ)Natural Language Understanding (NLU)Neural Text-to-Speech (NTTS)NeuroevolutionObjective FunctionPrecision and RecallPretrainingRecurrent Neural NetworksTransformersUnsupervised LearningVoice CloningZero-shot Classification Models
Acoustic ModelsActivation FunctionsAdaGradAI AlignmentAI Emotion RecognitionAI GuardrailsAI Speech EnhancementArticulatory SynthesisAssociation Rule LearningAttention MechanismsAugmented IntelligenceAuto ClassificationAutoencoderAutoregressive ModelBatch Gradient DescentBeam Search AlgorithmBenchmarkingBoosting in Machine LearningCandidate SamplingCapsule Neural NetworkCausal InferenceClassificationClustering AlgorithmsCognitive ComputingCognitive MapCollaborative FilteringComputational CreativityComputational LinguisticsComputational PhenotypingComputational SemanticsConditional Variational AutoencodersConcatenative SynthesisConfidence Intervals in Machine LearningContext-Aware ComputingContrastive LearningCross Validation in Machine LearningCURE AlgorithmData AugmentationData DriftDecision IntelligenceDecision TreeDeepfake DetectionDiffusionDomain AdaptationDouble DescentEnd-to-end LearningEnsemble LearningEpoch in Machine LearningEvolutionary AlgorithmsExpectation MaximizationFeature LearningFeature SelectionFeature Store for Machine LearningFederated LearningFew Shot LearningFlajolet-Martin AlgorithmForward PropagationGaussian ProcessesGenerative Adversarial Networks (GANs)Genetic Algorithms in AIGradient Boosting Machines (GBMs)Gradient ClippingGradient ScalingGrapheme-to-Phoneme Conversion (G2P)GroundingHuman-in-the-Loop AIHyperparametersHomograph DisambiguationHooke-Jeeves AlgorithmHybrid AIImage RecognitionIncremental LearningInductive BiasInformation RetrievalInstruction TuningKeyphrase ExtractionKnowledge DistillationKnowledge Representation and Reasoningk-ShinglesLatent Dirichlet Allocation (LDA)Learning To RankLearning RateLogitsMarkov Decision ProcessMetaheuristic AlgorithmsMixture of ExpertsModel InterpretabilityMultimodal AIMultitask Prompt TuningNamed Entity RecognitionNeural Radiance FieldsNeural Style TransferNeural Text-to-Speech (NTTS)One-Shot LearningOnline Gradient DescentOut-of-Distribution DetectionOverfitting and UnderfittingParametric Neural Networks Part-of-Speech TaggingPrompt ChainingPrompt EngineeringPrompt TuningQuantum Machine Learning AlgorithmsRandom ForestRegularizationRepresentation LearningRetrieval-Augmented Generation (RAG)RLHFSemantic Search AlgorithmsSemi-structured dataSentiment AnalysisSequence ModelingSemantic KernelSemantic NetworksSpike Neural NetworksStatistical Relational LearningSymbolic AITokenizationTransfer LearningVoice CloningWinnow AlgorithmWord Embeddings
Last updated on September 29, 202313 min read

Overfitting and Underfitting

Overfitting refers to a model that models the training data too well but fails to generalize to new data, while underfitting refers to a model that fails to capture the underlying pattern in the training data.

Machine Learning (ML) is a subset of artificial intelligence focused on building algorithms that enable computers to learn from and make decisions based on data. Rather than being explicitly programmed to perform a task, a machine learning algorithm uses statistical methods to learn from examples.

Importance of Achieving Balance Between Overfitting and Underfitting

Achieving a balance between overfitting and underfitting is crucial in machine learning model development. Overfitting occurs when a model learns the training data too well, capturing noise and anomalies as if they were true patterns, leading to poor generalization to unseen data. Conversely, underfitting happens when a model fails to capture the underlying trends in the data, resulting in poor performance both on the training and the unseen data. Striking the right balance is essential, as it ensures that the model is complex enough to capture underlying patterns but also general enough to perform well on new, unseen data.

Key Definitions

In the domain of machine learning, certain terms lay the foundation for understanding complex concepts, such as overfitting and underfitting. Grasping these fundamental terms will enable a clearer insight into the mechanics and nuances of the field. Here are some pivotal definitions to get started:

  • Machine Learning Model:
    An algorithm designed to recognize patterns within data, adapt to them, and utilize this understanding to make predictions or decisions. These models improve their accuracy over time by iteratively learning from both their successes and mistakes.

  • Training Data:
    This is the dataset on which machine learning models are trained. By exposing the model to this data, it learns the underlying patterns and relationships, thereby refining its algorithms and parameters.

  • Testing Data:
    A separate subset of data that the model has never seen before. It is used to evaluate how well the trained model generalizes its learning to new, unseen scenarios. The performance on testing data gives insights into the model’s real-world applicability.

  • Overfitting:
    A modeling error which occurs when a machine learning model is too closely tailored to the training data. It’s akin to studying so specifically for an exam that one is thrown off by any question not directly memorized. As a result, while performance on the training data might be exceptional, the model struggles to generalize to unseen data, capturing noise and anomalies as if they were genuine patterns.

  • Underfitting:
    This occurs when a model is too simplistic to grasp the underlying structures within the training data. In this scenario, the model needs to perform better on the training data and generalize effectively to new data. It’s as if one studied only basic concepts for an advanced exam, missing out on the subject's depth and breadth.

Causes and Characteristics

In the vast sphere of machine learning, achieving a model’s optimal performance can sometimes be akin to walking a tightrope. Two of the most common stumbling blocks faced are overfitting and underfitting. To better understand and navigate these pitfalls, one must delve into their underlying causes and the symptoms they exhibit.


Overfitting arises when a model becomes too attuned to its training data, attempting to capture every minuscule detail—even those that might be mere noise or random fluctuations. While seemingly beneficial, this precision hinders the model when it encounters new, unseen data.


  • High model complexity: Models with excessive parameters or layers can become unduly complex, capturing irrelevant specifics rather than broader patterns.

  • Limited training data: A scarce or non-diverse dataset can lead a model to hone in on particulars that don’t generalize well.

  • Memorization of noise: The model might latch onto random or inconsequential fluctuations rather than identifying genuine data patterns.


An overfitting model will often exhibit a high accuracy when tested on its training data, a performance that deceptively paints a promising picture. However, its true colors emerge when it poorly generalizes to new or unseen data.


In contrast, Underfitting occurs when a model remains too simplistic, failing to capture the data's foundational patterns and trends.


  • Low model complexity: Overly simplistic models may need more depth or nuance to understand data intricacies.

  • Inadequate learning of patterns: The model may skim the surface, missing out on crucial data relationships.


Models that underfit are marked by their consistently lackluster performance. They don’t shine on their training data and similarly fall short when introduced to new data. By failing on both fronts, such models signify a need for a more robust or refined learning approach.

Mitigating Strategies

Navigating the challenges of overfitting and underfitting requires understanding their causes and symptoms and a toolkit of strategies to mitigate their effects. These techniques enable practitioners to refine their models to strike a balance, ensuring optimal performance and generalization capabilities.

Combatting Overfitting

Overfitting, while a sign of a model’s zeal to capture data intricacies, can lead to poor generalization when encountering new data.

Regularization (L1, L2, ElasticNet) introduces a penalty on the magnitude of model parameters. This ensures the model doesn’t lean too heavily on any single feature and is less likely to capture noise. L1, L2, and ElasticNet are different forms of these penalties, each offering its unique advantages.

Pruning is especially pertinent for decision trees. The model is simplified by trimming parts of the tree that don’t provide substantial predictive power, making it less prone to overfitting.

Cross-validation partitions the training data into several subsets, training the model on different combinations. Its robustness is ensured by assessing the model’s performance across multiple data samples.

Data augmentation, especially useful for image datasets, creates new training samples through transformations like rotations and zooms. This diversifies the training data and reduces the model’s dependence on specific features.

Lastly, Dropout is a regularization method used in neural networks. It involves deactivating random subsets of neurons during training. This prevents any single neuron or group from becoming overly specialized, enhancing the model’s generalization capabilities.

Addressing Underfitting

Underfitting suggests the model hasn’t sufficiently grasped the core patterns of the data. Overcoming this requires amplifying the model’s learning capability.

The model can potentially discern more subtle data patterns by increasing model complexity, such as adding more layers in neural networks or creating deeper trees in decision forests.

Feature engineering involves introducing or transforming input features. With domain expertise and creativity, new, informative features can be developed, helping the model uncover deeper data relationships.

Sometimes, a model simply needs more training data, especially if the existing dataset needs more size or diversity. Introducing additional data can offer the model more examples to learn from.

Lastly, ensemble methods like bagging and boosting combine multiple models’ predictions. Through this collective intelligence, models can often improve overall performance, compensating for individual weaknesses.

Evaluation and Diagnostic Techniques

In the machine learning model development journey, evaluation is a crucial milestone. Properly gauging a model’s performance and diagnosing issues like overfitting or underfitting involves a suite of techniques. These methodologies ensure accurate and generalizable models, paving the way for reliable deployments.

Loss curves and Learning curves visually represent a model’s progress throughout its training phase. While loss curves depict how the model’s error rate changes over time, learning curves contrast training performance against validation performance. A divergence between these curves, especially in later epochs, can be indicative of overfitting, where the model performs well on training data but struggles with unseen data.

Cross-validation scores provide a more holistic view of a model’s performance. Cross-validation offers insights into how the model might perform under varied data scenarios by partitioning the data into multiple subsets and evaluating the model on different combinations. Consistent scores across different partitions suggest a robust model, while substantial variances might indicate overfitting.

The Confusion matrix and associated metrics like Precision, Recall, F1 Score, and ROC-AUC are essential tools for classification problems. The confusion matrix provides a detailed breakdown of true positives, true negatives, false positives, and false negatives. Derived from this matrix, Precision (a measure of result relevancy) and Recall (a measure of how many truly relevant results are returned) offer nuanced insights. The F1 Score balances these two metrics, and the ROC-AUC score (Receiver Operating Characteristic - Area Under Curve) gauges a model’s ability to discriminate between classes, providing a single number summary of classifier performance.

Model selection through Hyperparameter tuning acts as a fine-tuning mechanism. Hyperparameters, which aren’t directly learned during training, influence a model’s structure and its learning process. By systematically adjusting these parameters and evaluating performance, practitioners can hone in on an optimal model configuration, thereby potentially ameliorating issues like overfitting or underfitting.

When wielded judiciously, these evaluation and diagnostic tools offer practitioners a roadmap to refine models, ensuring they’re primed for real-world challenges.

Overfitting and Underfitting in NLP

Natural Language Processing (NLP) is uniquely positioned in the machine learning landscape. The nuances of human language, coupled with its vastness and variability, introduce a distinct set of challenges, making the phenomena of overfitting and underfitting even more pronounced.

Challenges and Characteristics

One of the main challenges in NLP is the high-dimensional data. Words, phrases, or sentences can be represented as vectors in a space that spans thousands or even millions of dimensions. This high dimensionality can make models prone to overfitting, especially when training data is limited.

Sparsity is another hurdle. While the vocabulary of a language is extensive, only a fraction of it is used in any given dataset. Models can overfit to the specific words and phrases they’ve seen, struggling to generalize to new, unseen words.

Further complicating the matter are sequences and temporal dependencies. Unlike other forms of data, language has a sequential nature—preceding words can influence each word’s meaning. Models can easily overfit to specific sequences in the training data, ignoring broader patterns that could help in generalization.

Strategies and Solutions

Given the intricacies of language, NLP has birthed specialized techniques to combat overfitting and address underfitting.

Embeddings have become a cornerstone in NLP. Instead of using high-dimensional one-hot vectors, embeddings provide a dense representation of words in a lower-dimensional space. These representations capture semantic relationships, reducing dimensionality and sparsity.

Attention mechanisms address the sequential nature of language. They allow models, especially neural networks, to focus on specific parts of the input data, determining which segments are most relevant for a given task. This dynamic weighting reduces the chance of overfitting to specific sequences.

The rise of transfer learning has ushered in a paradigm shift in NLP. Instead of training models from scratch, transfer learning leverages pre-trained models, fine-tuning them on specific tasks. This not only combats overfitting by using knowledge gained from vast datasets but also helps in situations with limited data, mitigating underfitting.

Lastly, pre-trained language models like BERT, GPT, and their variants have set new standards in NLP performance. These models, trained on extensive corpora, capture intricate patterns of language. Fine-tuning them on specific tasks allows practitioners to achieve state-of-the-art results, effectively addressing both overfitting and underfitting.

While NLP poses its unique challenges in the context of overfitting and underfitting, the field has also innovated a slew of techniques to navigate these challenges, ensuring that machines understand and process language efficiently.

Real-World Examples and Implications

Overfitting and underfitting aren’t just abstract concepts limited to the domain of machine learning theory. They manifest in real-world applications, affecting outcomes, user experiences, and even posing ethical dilemmas.

Instances in Famous ML Models and Applications

The Netflix Prize Challenge, launched in 2006, provides an interesting case study. Teams worldwide were tasked with improving Netflix’s recommendation algorithm. While many teams developed intricate models with impressive performances on the provided dataset, some struggled when their solutions were applied to newer, unseen data— a classic manifestation of overfitting to specific dataset nuances.

Another instance arises in medical diagnosis models. With a limited set of patient data and a myriad of potential symptoms and interactions, models can overfit, making them reliable on known cases but less so on unfamiliar ones. On the flip side, models that are too simplistic may underfit, failing to capture the intricacies of complex diseases, leading to potential misdiagnoses.

Impact on Decision Making, Automation, and User Experience

In automated systems, such as self-driving cars, the implications of overfitting can be catastrophic. If a model is trained extensively on, say, clear weather conditions and urban landscapes but rarely on foggy scenarios or rural terrains, it might overfit to the former. Such a model could struggle when faced with an unfamiliar environment, endangering safety.

User experience, especially in personalized content recommendation systems like those on YouTube or Spotify, is directly influenced by the quality of the underlying models. Overfit models might provide repetitive content, thinking a user’s interest in a single watched video translates to an overarching preference. Underfit models might lack personalization altogether, serving generic, often irrelevant content.

Ethical Considerations and Risks

Overfitting and underfitting have deep ethical ramifications, especially in areas like credit scoring or job application screenings. Overfit models might be biased towards profiles that resemble the majority in the training data, inadvertently marginalizing minority groups. Underfit models might oversimplify, treating diverse applicants as homogeneous groups and ignoring individual merits.

Furthermore, in healthcare applications, the stakes are life and death. Models that don’t generalize well might recommend incorrect treatments, risking patients’ well-being.

Overfitting and underfitting are more than just modeling challenges. They’re intertwined with real-world outcomes, user satisfaction, and ethical complexities. Recognizing and mitigating them is a technical necessity and a moral imperative.


The realms of Machine Learning and Natural Language Processing are replete with complexities and nuances, and among these, the phenomena of overfitting and underfitting stand out as quintessential challenges. They serve as cautionary tales, reminding practitioners that achieving high training accuracy doesn’t necessarily translate to real-world efficacy.

At their core, overfitting and underfitting revolve around the delicate balance between a model’s ability to generalize and its fit to the training data. Overfit models, while adept at replicating training data outcomes, falter when faced with unseen data, having internalized noise and anomalies. Conversely, underfit models, in their simplistic nature, fail to capture the intricate patterns in the data, delivering sub-par performance even on known data.

In the vast and varied world of applications, from content recommendations to life-critical medical diagnoses, the implications of not addressing these phenomena are profound. They influence user experiences, decision-making processes, automation reliability, and even broach ethical boundaries.

Yet, as we’ve navigated through this exploration, it’s evident that the field is not passive in the face of these challenges. Techniques like embeddings in NLP, regularization methods, transfer learning, and the leveraging of pre-trained models exemplify the innovations designed to strike the right balance. The dynamic and ever-evolving landscape of ML and NLP showcases a relentless pursuit to refine models, ensuring they’re robust, reliable, and responsible.

In closing, while overfitting and underfitting are perennial challenges in the machine learning journey, they also catalyze innovations, steering the field towards better practices, novel techniques, and a deeper understanding of data’s intricacies. As the domain advances, the vigilance against these phenomena will remain paramount, ensuring that models not only learn but truly understand.

Unlock language AI at scale with an API call.

Get conversational intelligence with transcription and understanding on the world's best speech AI platform.

Sign Up FreeSchedule a Demo