Data Preprocessing

Feature discretization is the process of converting continuous features into discrete categories.

Feature scaling is a technique used to standardize the range of independent variables in data preprocessing.

Feature selection is the process of identifying and selecting important variables for machine learning models.

Min-Max normalization scales data to a fixed range, typically [0, 1], improving model performance in machine learning.

Min-Max Scaling is a normalization technique that scales features to a fixed range, typically [0, 1].

Normalization techniques adjust data to a common scale, improving model performance and interpretability in AI.

Normalized features are standardized input values used to improve AI model performance.

Normalized input refers to the process of adjusting data to a common scale in AI and machine learning.

One-Hot Encoding is a method for converting categorical data into a binary format for machine learning.

A padding operation adds extra data to inputs to ensure consistent size for processing in AI models.

SMOTE is a technique used to balance datasets by generating synthetic examples for underrepresented classes.

Stopword removal is the process of eliminating common words from text data to enhance analysis and processing efficiency.

Undersampling is a technique used in machine learning to balance datasets by reducing the number of instances in the majority class.