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clustering algorithm A typical unsupervised learning algorithm, which is mainly used to automatically classify similar samples into one category. In the clustering algorithm, samples are divided into different categories according to the similarity between samples. Different clustering results will be obtained for different similarity calc ...

Word2vec of recommendation system Objective: in the tasks related to natural language processing, we should hand over the natural language to the algorithm in machine learning. Usually, we need to mathematicize the language, because the machine is not a human, and the machine only recognizes mathematical symbols. Vectors are things that people ...

Case 1 (happiness prediction) Background introduction Happiness involves philosophy, psychology, sociology, economics and other disciplines. At the same time, it is closely related to everyone's life. Everyone has his own measurement standard for happiness. If we can find the commonalities that affect happiness and find the policy factors tha ...

github address: DataScicence Integrated learning 5-Xgboost principle and parameter tuning Integrated learning 4-forward step-by-step algorithm and GBDT principle and case Principles and cases of integrated learning 3-Boosting Principle and case analysis of integrated learning 2-bagging Principle and case analysis of integrated learning 1- votin ...

5.1 Classification learning The qualitative output of Classification problem is Classification, or discrete variable prediction. Regression problem, the quantitative output is regression, or continuous variable prediction. """ Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly. """ f ...

Original text: Li Xiaolai @ study and study againIt only takes one day to translate a book! ——This is what I did today.At the beginning of this month, I explored the Google Cloud Translation API for a while. In the process, I found DeepL and directly turned around. I bought the Pro service for less than $24 a month and less than $3 ...

Basic concepts of decision tree Decision tree is a tree. The leaf node corresponds to the decision result, and each other node corresponds to an attribute test;The sample set contained in each node is divided into sub nodes according to the results of attribute test;The root node contains the complete set of samples, and the path from the roo ...

This paper first writes the demo of the multi classification problem supported by xgboost, prints the tree structure, and then understands the principle of xgboost to realize the multi classification problem. This order is easier to understand. xgboost multi classification problem demo This demo can be seen from the source code of xgboost. In ...

Feather low is a 3D scan based image that I want to share with you today. The article will divide and explain the whole code. After reading it completely, I believe you will gain something. Welcome to "Yufeng codeword" catalogue 1. Project introduction 2. API preparation 3. Data set preparation 3.1 downloading data 3.2 data p ...

Density based approach: DBSCAN Visual website: https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/ DBSCAN = Density-Based Spatial Clustering of Applications with Noise This algorithm divides the region with high enough density into clusters, and can find clusters of any shape Several concepts 𝛆 neighborhood: the area w ...