Decision tree is the basic building block of gradient elevator and random forest. Visual decision tree is very helpful in learning the working principle and interpretability of these models. However, the current visualization package is still very rudimentary and does not help novices much.
When I visited Github recently, I found a great dtree ...
Posted by stallingjohn on Mon, 06 Dec 2021 11:47:30 -0800
1, Naive Bayes overview
Naive Bayesian method is a classification method based on Bayesian theorem and the assumption of feature conditional independence. For a given training set, firstly, the joint probability distribution of input and output is learned based on the independent assumption of characteristic conditions (the naive Bayes method, ...
Posted by yaatra on Sat, 27 Nov 2021 22:37:58 -0800
1. Problem description
Independent variables: the influencing factors of the optimal distillation structure are the feed composition A, B, C, the ratio of relative volatility ESI, and the separation requirements GESI, where A+B+C=1, so the degrees of freedom of the five independent variables are 4.
Dependent variable: there are 7 kinds of thr ...
Posted by Seamless on Fri, 19 Nov 2021 20:51:09 -0800
The best way to represent data depends not only on the semantics of the data, but also on the type of model used. linear model With tree based models (e.g Decision tree，Gradient lifting tree and Random forest )It is a very common model with many members. They have very different properties when dealing with different feature representat ...
Posted by xmanofsteel69 on Fri, 19 Nov 2021 18:52:07 -0800
Decision tree is a basic classification and regression method. This chapter mainly discusses the decision tree for classification. The decision tree model has a tree structure. In the classification problem, it represents the process of classifying instances based on features. It can be considered as a set of if then rules, or as a conditional ...
Posted by achilles on Mon, 08 Nov 2021 23:05:06 -0800
1, ID3 algorithm
import numpy as np
import pandas as pd
import sklearn.tree as st
import matplotlib.pyplot as plt
2. Read data
data = pd.read_csv('C:/Watermelon dataset.csv',header=None)
mD = len(dataSet)
dataLabelList = [x[-1] f ...
Posted by CWebguy on Sat, 06 Nov 2021 10:26:57 -0700
ID3 decision tree algorithm
ID3 algorithm was first proposed by J. Ross Quinlan at the University of Sydney in 1975. The core of the algorithm is "information entropy". By calculating the information gain of each attribute, ID3 algorithm considers that the attribute with high information gain is a good attr ...
Posted by apervizi on Sun, 31 Oct 2021 03:13:31 -0700
1, What is a decision tree
Decision tree is a method of machine learning. The generation algorithms of decision tree include ID3, C4.5 and C5.0. Decision tree is a tree structure, in which each internal node represents a judgment on an attribute, each branch represents the output of a judgment result, and finally each leaf node represents ...
Posted by Boris Senker on Sat, 30 Oct 2021 22:33:25 -0700
1, Introduction to CART decision tree algorithm
CART (Classification And Regression Trees) algorithm is a tree construction algorithm, which can be used for both classification tasks and regression. Compared with ID3 and C4.5, which can only be used for discrete data and classification tasks, CART algorithm has a much wider application. It can ...
Posted by elklabone on Wed, 27 Oct 2021 00:59:01 -0700
1, Decision tree
Decision Tree (Decision Tree) is a decision analysis method that calculates the probability that the expected value of net present value is greater than or equal to zero by forming a Decision Tree on the basis of knowing the occurrence probability of various situations, evaluates the project risk and judges its feasibility ...
Posted by Aretai on Tue, 26 Oct 2021 04:53:39 -0700