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Python Programming Learning Circle
Python Programming Learning Circle
Sep 11, 2025 · Artificial Intelligence

Essential Machine Learning Algorithms: From Linear Regression to DBSCAN

This article provides a comprehensive overview of key machine‑learning algorithms—including supervised methods like linear regression, SVM, Naive Bayes, logistic regression, k‑NN, decision trees, random forests, GBDT, and unsupervised techniques such as k‑means, hierarchical clustering, DBSCAN, and PCA—explaining their principles, strengths, and typical use cases.

AlgorithmsNaive BayesUnsupervised Learning
0 likes · 20 min read
Essential Machine Learning Algorithms: From Linear Regression to DBSCAN
Model Perspective
Model Perspective
Jun 18, 2022 · Artificial Intelligence

Understanding Naive Bayes: Theory, Example, and Practical Steps

This article introduces the Naive Bayes classifier, explains its independence assumptions, walks through a weather‑based example with detailed probability calculations, demonstrates how to build and apply the model, and highlights its strengths and limitations in real‑world tasks such as document classification and spam filtering.

Naive Bayessupervised learning
0 likes · 7 min read
Understanding Naive Bayes: Theory, Example, and Practical Steps
Python Programming Learning Circle
Python Programming Learning Circle
Dec 18, 2020 · Artificial Intelligence

Understanding the Bayesian Formula and Naive Bayes Classifiers with Scikit-learn

This article explains the Bayesian theorem, introduces the Bayesian classifier, and details three Naive Bayes algorithms—Gaussian, Multinomial, and Bernoulli—along with their Scikit-learn implementations, key parameters, attributes, methods, and typical text‑classification applications for spam filtering.

BayesianNaive Bayesartificial intelligence
0 likes · 8 min read
Understanding the Bayesian Formula and Naive Bayes Classifiers with Scikit-learn
21CTO
21CTO
Sep 18, 2020 · Artificial Intelligence

Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know

This article provides a concise overview of ten fundamental machine learning algorithms—linear regression, logistic regression, linear discriminant analysis, naive Bayes, K‑nearest neighbors, learning vector quantization, decision trees, random forest, support vector machines, and boosting (AdaBoost)—explaining their core concepts, typical use‑cases, and practical considerations.

Naive BayesRandom ForestSupport Vector Machine
0 likes · 13 min read
Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know
21CTO
21CTO
Apr 12, 2019 · Artificial Intelligence

Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know

This article provides a concise overview of ten fundamental machine learning algorithms—linear regression, logistic regression, linear discriminant analysis, naive Bayes, K‑nearest neighbors, learning vector quantization, support vector machines, decision trees, bagging/random forest, and boosting/AdaBoost—explaining their principles, typical use cases, and key characteristics.

Naive BayesRandom ForestSupport Vector Machine
0 likes · 13 min read
Top 10 Essential Machine Learning Algorithms Every Data Scientist Should Know
JD Tech
JD Tech
Feb 12, 2019 · Artificial Intelligence

Content‑Based Filtering: Concepts, Implementation, and Pros/Cons

The article explains content‑based filtering for recommendation systems, covering its basic concepts, feature requirements, implementation using vector representations and cosine similarity, advantages and disadvantages, and supplementary algorithms such as k‑Nearest Neighbor, Rocchio, decision trees, linear classifiers, and Naive Bayes.

Naive BayesRocchiocontent-based filtering
0 likes · 11 min read
Content‑Based Filtering: Concepts, Implementation, and Pros/Cons
Qunar Tech Salon
Qunar Tech Salon
Jan 16, 2019 · Artificial Intelligence

Introduction to Naive Bayes Classifier with scikit-learn

This article introduces the Naive Bayes classification algorithm, explains its theoretical basis, demonstrates how to use scikit-learn's GaussianNB class with Python code, evaluates model performance, and discusses advantages, limitations, and practical examples of the method.

Naive BayesPythonclassification
0 likes · 11 min read
Introduction to Naive Bayes Classifier with scikit-learn
MaGe Linux Operations
MaGe Linux Operations
Apr 8, 2018 · Artificial Intelligence

Master Python Data Mining & Machine Learning: From Preprocessing to Classification

This comprehensive tutorial walks you through Python data mining and machine learning fundamentals, covering data preprocessing techniques, common classification algorithms, an Iris flower classification case study, and practical tips for selecting the right algorithm, all illustrated with clear code examples and visualizations.

Classification AlgorithmsNaive BayesPython
0 likes · 22 min read
Master Python Data Mining & Machine Learning: From Preprocessing to Classification
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Mar 6, 2018 · Artificial Intelligence

Master Naive Bayes: From Theory to Python Text Classification

This article introduces the Naive Bayes classifier, explains its underlying probability formulas—including conditional probability, total probability, and the Bayes theorem—covers the feature independence assumption, Laplace smoothing, and demonstrates both manual and scikit‑learn implementations for email and text classification with Python code.

Naive Bayesprobabilityscikit-learn
0 likes · 11 min read
Master Naive Bayes: From Theory to Python Text Classification
Hulu Beijing
Hulu Beijing
Mar 1, 2018 · Artificial Intelligence

Understanding Probabilistic Graphical Models: Bayesian & Markov Networks Explained

This article introduces probabilistic graphical models, explains the differences between Bayesian and Markov networks, derives their joint probability distributions, and details the principles and graphical representations of naive Bayes and maximum entropy models with illustrative equations and diagrams.

Bayesian networkNaive Bayesmarkov network
0 likes · 10 min read
Understanding Probabilistic Graphical Models: Bayesian & Markov Networks Explained
ITPUB
ITPUB
Feb 23, 2016 · Fundamentals

Master 10 Essential Algorithms: From QuickSort to Naive Bayes

This guide introduces ten core algorithms—including QuickSort, HeapSort, MergeSort, Binary Search, BFPRT, DFS, BFS, Dijkstra, Dynamic Programming, and Naive Bayes—explaining their principles, step‑by‑step procedures, and typical use cases for efficient problem solving.

AlgorithmsNaive BayesSearch
0 likes · 14 min read
Master 10 Essential Algorithms: From QuickSort to Naive Bayes
Suning Technology
Suning Technology
Jun 18, 2015 · Artificial Intelligence

How Suning Uses Naive Bayes for High‑Accuracy Product Classification

This article explains Suning's implementation of a Naive Bayes‑based product classification system, detailing its basic theory, formal definition, step‑by‑step training process, three implementation phases, evaluation results, and error analysis to improve classification accuracy.

Naive BayesSuningalgorithm
0 likes · 6 min read
How Suning Uses Naive Bayes for High‑Accuracy Product Classification
Meituan Technology Team
Meituan Technology Team
Dec 18, 2014 · Artificial Intelligence

Auto-Label Missing POI Categories Using Naive Bayes and Feature Selection

This article details a step‑by‑step machine‑learning pipeline that transforms over one million calibrated POI records into feature vectors, selects discriminative terms via information‑gain and domain rules, trains a Naive Bayes classifier, and achieves 91% accuracy with 84% coverage on unseen POI data.

Chinese NLPNaive BayesPOI classification
0 likes · 12 min read
Auto-Label Missing POI Categories Using Naive Bayes and Feature Selection