1. Linear Regression Linear regression is a statistical method used to study the relationship between two continuous variables: one independent variable and one dependent variable. The goal of linear regression is to find the best-fit line through a set of data points, which can then be used to predict future observations. The equation for a … Read more
Click on the above“Beginner Learning Vision” to select “Star” or “Pin” Important content delivered at the first time Linear Models Linear models are the simplest and most basic machine learning models. Their mathematical form is as follows: g(x;w)=. Sometimes, we also add a bias term b on top of, but as long as we expand … Read more
This session shares as the title: What Exactly Is Machine Learning A Beginner’s Tutorial to Understand Machine Learning //// //// First Steps in Machine Learning Of course, it’s to understand its basic concepts This article summarizes insights from the internet, personal learning notes✍ What is machine learning, and why does it have such great power? … Read more
Source: Zhihu Abner says AI This article is approximately 4500 words long and suggests a reading time of 9 minutes. This article summarizes the pros and cons of the top 10 machine learning algorithms. 1. Logistic Regression The binary logistic regression model is a classification model represented by the conditional probability distributionP(Y|X), which takes the … 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
1. Logistic Regression The binary logistic regression model is a classification model represented by the conditional probability distributionP(Y|X), with the form of a parameterized logistic distribution. Here, the random variable X takes real values, and the random variable Y takes values of 1 or 0. The model parameters can be estimated using a supervised method. … Read more
Originally from Data Analysis and Applications Machine learning models have powerful and complex mathematical structures. Understanding their intricate workings is an important aspect of model development. Model visualization is crucial for gaining insights, making informed decisions, and effectively communicating results. In this article, we will delve into the art of machine learning visualization, exploring various … Read more
Both linear models and decision trees are important types of machine learning.We have spent a lot of time learning linear regression; do you feel like you have opened the door to machine learning?Next, we continue to explore machine learning, from understanding to application. 1. The Role of Prediction and Classification in Machine Learning The purpose … 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
Official WeChat account of Tsinghua Big Data Software Team Source: Zhihu This article is about 5000 words long, and it is recommended to read for 5 minutes. This article systematically summarizes the related content about decision trees, random forests, etc. 1、Decision Tree A decision tree is a supervised classification model that essentially selects a feature … Read more