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Cross-Validation is a core method for assessing the generalization ability of a model in machine learning.The basic idea is to split the original data into a training set and a test set, and to obtain more reliable performance estimates by rotating the use of different data subsets for training and validation. This approach simulates ...

Random Forest (Random Forest) is an integrated learning algorithm that accomplishes machine learning tasks by constructing multiple decision trees and synthesizing their predictions. The algorithm is based on the Bootstrap aggregation idea, where multiple subsets of samples are randomly drawn from the original dataset with putback for each tree...

Loss Function (Loss Function) is a core concept in Machine Learning, undertaking the important task of quantifying the prediction error of a model. This function mathematically measures the degree of difference between the model's predicted value and the true value, providing a clear directional guide for model optimization.

In machine learning, a hyperparameter is a configuration option that is preset manually before model training begins, rather than learned from data. The central role is to control the learning process itself, as if setting a set of operating rules for the algorithm. For example, the learning...

Decision Tree (DT) is a tree-shaped predictive model that simulates the human decision-making process, classifying or predicting data through a series of rules. Each internal node represents a feature test, branches correspond to test results, and leaf nodes store the final decision. This algorithm uses a divide-and-conquer strategy...

Gradient Descent is the core optimization algorithm for solving function minimization. The algorithm determines the direction of descent by calculating the gradient of the function (the vector consisting of the partial derivatives of each), and iteratively updating the parameters according to the rule θ = θ - η - ∇J(θ).

Logistic Regression is a statistical learning method used to solve binary classification problems. The central goal is to predict the probability that a sample belongs to a particular category based on input features. The model maps the linear output to between 0 and 1 by linearly combining the eigenvalues using an S-shaped function...

Regularization is a core technique in machine learning and statistics to prevent model overfitting. Regularization controls the degree of fitting by adding a penalty term to the objective function that is related to the complexity of the model. Common forms include L1 and L2 regularization: the L1 produces sparse solutions and applies...

Generative Adversarial Network (GAN) is a deep learning model proposed by Ian Goodfellow et al. in 2014. The framework implements generative modeling by training two neural networks against each other...

Self-Attention is a key mechanism in deep learning, originally proposed and widely used in the Transformer architecture. The core idea is to allow the model to simultaneously attend to all positions in the input sequence, and compute each position by weighted aggregation of...