Training Course Category

RABIULISLAM880

Everyone is a product manager Home Page  Browse Activities Lectures Q&A Corporate Training Fishing News Search Registration Login Seven Commonly Used Machine Learning Algorithms Detailed Lectures on Decision Trees and Random Forests Three Fire Granules Product Attention - Comments Browse Collection Minute-End Product Manager Needs Product managers need to consider more about the functionality, stability, security, compliance, etc. of the product. This article will provide an in-depth analysis of the working principles of the two algorithms, decision tree and random forest. The advantages, disadvantages and practical applications lead readers to explore the intelligent decision-making mechanism behind it. Decision trees and random forests


As two powerful supervised learning models, they occupy an Rich People Phone Number List important position in classification and regression tasks because of their characteristics of being intuitive, easy to understand, highly interpretable and suitable for various problems. 1. The selection path from simple to complex decision tree. Basic principles of decision tree. Decision tree is a model for decision-making based on tree structure. It divides data through a series of rules.



According to the space, a preset judgment process is formed. Each internal node represents a feature test, each branch represents an output value of this feature, and each leaf node corresponds to a category or regression value. The process of building a decision tree is to find the optimal segmentation attributes to recursively partition the data set in a way that maximizes the information gain I or the Gini impurity R. I switched jobs to be a terminal product manager and prepared to do something big. I found that I had simplified the problem. In recent years, the vigorous development of the terminal business has also made many related positions more difficult.