TopAIToolsTopAITools

Glossary

0-9
0-shot learning1-shot learning2-stage detector3D convolution4D data5G + AI6DoF pose estimation7D representation8-bit quantization9-layer network
A
AGI / Artificial General IntelligenceAlgorithmArtificial Intelligence (AI)AttentionAutoencoder
B
BackpropagationBatch NormalizationBERTBiasBoosting
C
ChatbotClassifier / ClassificationClusteringCNN / Convolutional Neural NetworkCross-Validation
D
Data AugmentationDeep LearningDeepfakeDeterministic ModelDiscriminative Model
E
EmbeddingEncoderEnsemble LearningEpochExplainable AI (XAI)
F
Feature ExtractionFine-tuningForward PropagationFoundation ModelFusion / Multimodal Fusion
G
GAN / Generative Adversarial NetworkGenerative AIGradient DescentGraph Neural Network (GNN)Grounding
H
HallucinationHeuristicHidden LayerHierarchical ModelHyperparameter
I
Imbalanced DataInstance / SampleInstruction tuningIntelligence Amplification / AugmentationInterpretability
J
JAXJitteringJoint EmbeddingJSONL / JSON-linesJuxtaposition
K
K-means ClusteringK-Shot LearningKernel TrickKL Divergence (Kullback–Leibler Divergence)Knowledge Distillation
L
Large Language Model (LLM)Latent VariableLearning RateLoss FunctionLSTM / Long Short-Term Memory
M
Machine Learning (ML)Meta-learningModelMulti-head AttentionMultimodal / Multimodality
N
Neural NetworkNLP / Natural Language ProcessingNLU / Natural Language UnderstandingNormalizationNovelty Detection / Anomaly Detection
O
Objective FunctionOne-hot EncodingOnline LearningOptimizerOverfitting
P
ParameterPolicy / Reinforcement Learning PolicyPoolingPretrainingPrompt
Q
Q-learningQuality EstimationQuantizationQueryQueue / Buffer
R
RegularizationReinforcement Learning (RL)Representation LearningRetrieval Augmented Generation (RAG)RNN / Recurrent Neural Network
S
SamplingSelf-Supervised LearningSequence ModelingSoftmaxSupervised Learning
T
TokenizerTraining DataTransfer LearningTransformerTuning / Hyperparameter Tuning
U
U-NetUncertainty EstimationUnderfittingUniversal Approximation TheoremUnsupervised Learning
V
Validation SetVanishing / Exploding GradientVariational Autoencoder (VAE)Vector EmbeddingVision Transformer (ViT)
W
Weak SupervisionWeight DecayWhitening / Whitening TransformationWord EmbeddingWorkflow
X
X-axis / feature axisXAI / Explainable AIXLMXLNetXOR problem
Y
Y-axis / feature axisY-transform / YUVYAGNI (You Aren't Gonna Need It)Yield (model yield / throughput)Yoga of AI
Z
Z-score NormalizationZero-centric / Zero-bias initializationZero-gradient phenomenonZero-shot Learning / Zero-shot inferenceZygosity in augmentation

What is Overfitting

Overfitting is a crucial concept in machine learning and statistical modeling, referring to a model that performs well on training data but poorly on new, unseen data. This phenomenon typically occurs when the model is too complex or when there is insufficient training data. When a model learns the noise in the training data instead of the underlying patterns, it leads to overfitting.


Overfitting is an important metric for model evaluation, particularly in machine learning. It involves the model's ability to generalize, meaning how well it performs on data it has not encountered before. The issue is not limited to machine learning; it can also be observed in statistical analysis, making it essential to find an appropriate complexity for the model to ensure it accurately reflects training data while effectively predicting new data.


During training, the model adjusts its parameters through optimization algorithms to minimize training error. If the model is too complex, it may fit all the fluctuations and anomalies in the training set, rather than just the true trends in the data. Common solutions include cross-validation, regularization (such as L1 and L2), and simplifying the model structure.


A common instance of overfitting is seen in decision tree models; when the tree depth is excessive, it may overly adapt to the noise in the training data, resulting in poor performance on new datasets. Conversely, simpler linear models are less likely to overfit, even if they may not perform as well on complex datasets.


As deep learning technologies evolve, the problem of overfitting remains an active research area. Researchers continuously explore new methods to improve model generalization capabilities, employing techniques such as ensemble learning, transfer learning, and Generative Adversarial Networks (GANs).


The primary advantage of overfitting is the model's ability to accurately reflect the training data, but the downside is that it can lead to decreased performance in real-world applications. While methods to prevent overfitting are effective, they may also result in underfitting, meaning the model is too simple to capture the complexity of the data.


When addressing overfitting, it is vital to balance model complexity with the true patterns in the data. Data preprocessing, feature selection, and model evaluation are all key steps in preventing overfitting.