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1 RandomForestClassifier 1.1 parameters of control based evaluator 1.2 n_estimators n_ The larger the estimators, the better the effect of the model. But correspondingly, any model has a decision boundary, n_ After the estimators reach a certain degree, the accuracy of random forest often does not rise or begin to fluctuate, and n_ The lar ...

1, Theoretical knowledge purity For a branch node, if the samples contained in the node belong to the same category, its purity is 1, and we always hope that the higher the purity, the better, that is, as many samples belong to the same category as possible. So how to measure "purity"? Therefore, the concept of "information ...

catalogue   Steps of decision tree learning Advantages and disadvantages of decision tree Generate decision tree for example code   Decision tree is a tree structure. Each internal node represents the judgment of an attribute, each branch represents the output of a result, and the last leaf node represents the result of a classif ...

1, Decision tree 1.1 INTRODUCTION Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which calculates the probability that the expected value of net present value is greater than or equal to zero by forming a decision tree, evaluates the project risk and judges its feasibility. ...

1, ID3 algorithm 1. Pseudo code ID3 (Examples, Target_Attribute, Attributes) Create a root node for the tree If all examples are positive, Return the single-node tree Root, with label = +. If all examples are negative, Return the single-node tree Root, with label = -. If number of predicting attributes is empty, then Retur ...

Experimental background In previous experiments: Machine learning_ 1:K-nearest neighbor algorithm Machine learning_ 2:K-nearest neighbor algorithm We have learned that K-nearest neighbor algorithm is an algorithm that can be used for classification without training, but it also has many disadvantages. The biggest disadvantage is that it ca ...

This experiment will realize a simple binary decision tree. I wanted to finish my homework without being familiar with the theory. As a result, I encountered a bottleneck... So I began to organize my ideas carefully from the beginning. It seems that the shortcuts taken will eventually be redoubled. Knowledge should be accumulated honestly. It's ...

In machine learning, random forest is a classifier containing multiple decision trees. It is a set algorithm, and its output category is determined by the mode of the category output by individual trees. Random forest = Bagging + decision tree Bagging integration principle bagging integration process 1. Sampling: take a part of all samples 2 ...

Experimental introduction 1. Experimental contents This experiment learns and implements the decision tree algorithm. 2. Experimental objectives Through this experiment, master the basic principle of decision tree algorithm. 3. Experimental knowledge points Shannon entropyinformation gain 4. Experimental environment python 3.6.5 5. Pr ...

Summary: random forest can be used for classification and regression as decision tree, but the results of random forest model are often better than decision tree. This article mainly explains the principles and common parameters of the above two ML algorithms. 1, Principle 1.1 decision tree 1.1.1 definition of decision tree Decision tree is ...