KNN (K-Nearest Neighbors) algorithm is a simple and intuitive supervised learning algorithm, widely used for classification and regression tasks. This article will guide you step by step on how to implement the KNN algorithm using the scikit-learn library in Python, and we will practice with the Iris dataset. Let’s explore how to classify the Iris … Read more
Click on the above “Beginner’s Guide to Vision“, select to add “Star” or “Pin“ Essential insights delivered promptly Introduction The idea of KNN (k-Nearest Neighbors) is simple, and the mathematical knowledge required is almost zero, making it very practical as an introduction to machine learning. It can explain many details during the use of machine … Read more
Author: Ben Sanders Translator: Wu Huicong Proofreader: Zheng Zi This article contains approximately 2400 words, and it is recommended to read in 10 minutes. This article will briefly introduce what machine learning is, how it works, and two main algorithms in machine learning. Introduction In this article, Greg Lamp, co-founder of the data science startup … Read more
The Gradient Boosting Decision Tree (GBDT), as a leader in ensemble learning, has won the favor of many data scientists due to its excellent performance in classification and regression tasks. This article will take you deep into the mysterious veil of GBDT and demonstrate how to efficiently implement GBDT using the sklearn library. 1. What … Read more
Source: CSDN Blog Algorithm Advancement This article is about 9700 words, and it is recommended to read in 5 minutes. Today, I will introduce a great beginner article about machine learning. For programmers, the importance of machine learning goes without saying. Maybe you haven’t started yet, or maybe you’ve failed before, but it doesn’t matter, … Read more
Originally from Python Artificial Intelligence Frontier In simple terms, a machine learning model is a type of mathematical function that maps input data to predicted outputs. More specifically, a machine learning model is a mathematical function that adjusts model parameters through learning from training data to minimize the error between predicted outputs and actual labels. … Read more
Author: Mathieu Carrière Translator: Sun Taochun Proofreader: He Zhonghua This article is approximately 4500 words long and is recommended to be read in 10minutes. This article briefly introduces the power of Topological Data Analysis (TDA) in machine learning and demonstrates how to practice with three Python libraries: Gudhi, Scikit-Learn, and Tensorflow. Tags: Data Visualization Hi … Read more
What is Scikit-learn? Scikit-learn is an open-source Python library that includes various unsupervised and supervised learning techniques. It is built on technologies and libraries such as Matplotlib, Pandas, and NumPy, which help simplify coding tasks. Features of Scikit-learn include: Classification (including K-Nearest Neighbors) Preprocessing (including Min-Max normalization) Clustering (including K-Means++ and K-Means) Regression (including Logistic … Read more
A simple technique for integrating decision trees involves training trees on subsamples of the training dataset. A subset of rows from the training data can be used to train individual trees known as bagging. When a subset of rows from the training data is also used when calculating each split point, this is referred to … Read more
Extreme Gradient Boosting (XGBoost) is an open-source library that provides an efficient implementation of the gradient boosting algorithm. Although other open-source implementations of this method existed before XGBoost, the release of XGBoost seems to have unleashed the power of the technique and brought gradient boosting to the attention of the machine learning community at large. … Read more