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Chimerge algorithm and matlab code implementation (data discretization) 1. Algorithm analysis (chi square test) Is the deviation degree between the actual observation value and the theoretical inference value of the statistical sample. The deviation degree between the actual observation value and the theoretical inference value determines th ...

Super parameter optimization methods: grid search, random search, Bayesian Optimization (BO) and other algorithms. ​ reference material: Three super parameter optimization methods are explained in detail, as well as code implementation ​ Experimental basic code import numpy as np import pandas as pd from lightgbm.sklearn import LGBMRegr ...

Kaggle case: predictive classification of cardiac patients based on a random forest Hello, my name is Peter~ A kaggle case shared today: predictive classification of cardiac patients based on the RandomForest model. The knowledge points involved in this paper mainly include: Data preprocessing and type transformationEstablishment and Interpr ...

[translated from: Dual Annealing Optimization With Python] [Note: Jason Brownlee likes PhD's articles very much, so he will do some translation and learning practice in his spare time. Here is the practice record of the corresponding work, hoping to help people in need!] Dual Annealing is a stochastic global optimization algorithm. It is an i ...

Background knowledge required for reading this article: linear support vector machine and yidui programming knowledge1, Introduction   previously, we introduced two support vector machine models in two sections - hard interval support vector machine and soft interval support vector machine. These two models can be collectively referre ...

catalogue Walk into the decision tree Case 1 Case 2 Tree of soul Selection mechanism of decision tree Measurement rules of node purity Measurement of purity Background introduction of decision tree algorithm Information and quantification of information Information entropy Conditional entropy Information Gain (used by ID3 algorithm) ...

Support Vector Machine Mathematical proof Pre knowledge: Lagrange number multiplication, dual problem, kernel technique Lagrange number multiplication For constrained optimization problems: Example: Known x>0,y>0,x+2y+2xy=8,be x+2y Minimum value of__. Solution: Introduction parameters λ ...

Principles Principle here , we can see from the principle that the K-means algorithm mainly has three parts - random initialization, clustering division and moving aggregation. Random initialization Using the randperm function, the 1 ∼ m ...

LightGBM Main advantages of LightGBM: Easy to use. It provides the mainstream Python\C++\R language interface. Users can easily use LightGBM to model and obtain quite good results. Efficient and scalable. When dealing with large-scale data sets, it is efficient, fast and accurate, and has low requirements for hardware resources such as memory ...

2.1 introduction to machine learning process 2.1.1 overall process of machine learning Next, the implementation process of "supervised learning", which is the most widely used of three types of machine learning methods, is described. The implementation process of supervised learning can be summarized into the following steps: c ...