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The Naive Bayes algorithm is a supervised learning algorithm based on Bayes' theorem. Naive Bayes is based on Bayes' theorem. The "naive" part is the assumption that the features are conditionally independent of each other. Simplifying the assumptions greatly reduces the computational complexity and makes the algorithm efficient in practical applications.

K-Means Clustering (K-Means Clustering) is a classical unsupervised machine learning algorithm. It is mainly used to divide a dataset into K disjoint clusters. The goal of the algorithm is to assign n data points to the K clusters so that each data point belongs to the cluster corresponding to its nearest cluster center.

Feedforward Neural Network (FNN) is the basic and widely used artificial neural network model. The core feature is that the connections in the network do not form any loops or feedback paths, and the information flows strictly unidirectionally from the input layer to the output layer, after a...

K-Nearest Neighbors (K-Nearest Neighbors) are instance-based supervised learning algorithms that can be used for classification and regression tasks.

Convolutional Neural Network (CNN), an artificial neural network specifically designed to process data with a grid structure, has excelled in the field of image and video analysis.

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...