Data Preprocessing on English AI Terms Dictionary

Normalization

Sat, 18 Jul 2026 10:09:07 +0000

Definition

Common methods include Min-Max scaling and Z-score standardization. This process ensures that features with larger magnitudes do not dominate the learning algorithm, particularly in gradient-based optimization like neural networks. By normalizing input data, models train faster and achieve better stability. It is a critical step in preparing datasets for machine learning pipelines to ensure equitable contribution from all variables.

Summary

Normalization is a data preprocessing technique that scales numerical features to a standard range, typically between 0 and 1, to improve model convergence and performance.

Label noise

Sat, 18 Jul 2026 10:04:10 +0000

Definition

Label noise refers to discrepancies between the true class labels of data instances and the labels provided in the training dataset. This can arise from human annotation errors, ambiguous data points, or systematic labeling biases. Noise can be symmetric (random mislabeling) or asymmetric (specific classes mislabeled as others). It degrades model performance and generalization, necessitating robust learning techniques such as noise-tolerant loss functions, data cleaning, or ensemble methods to mitigate its adverse effects during training.

BPE

Sat, 18 Jul 2026 09:40:12 +0000

Definition

Byte Pair Encoding (BPE) is a data compression technique adapted for natural language processing to handle out-of-vocabulary words. It starts with a vocabulary of individual characters and iteratively merges the most frequent adjacent pairs of symbols. This process creates a hierarchy of subword units, allowing models to balance between character-level flexibility and word-level efficiency. It is widely used in transformer-based models like GPT-2 and BERT to manage vocabulary size while preserving semantic meaning across diverse languages.