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Random forest Random forest is a supervised learning algorithm, which is an integrated learning algorithm based on decision tree. Random forest is very simple, easy to implement, and the computational cost is very small. It shows amazing performance in classification and regression. Therefore, random forest is known as "a method represen ...

1, Algorithm Introduction    Decision Tree is a decision analysis method to obtain the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by forming a Decision Tree on the basis of knowing the occurrence probability of various situations. It i ...

Supervised algorithm decision tree 1: Algorithm overview Decision tree: includes classification tree / regression tree. Regression tree is rarely used, so classification tree is mainly introduced here Algorithm flow: feature selection – > decision tree generation – > decision tree pruning 2: Feature selection 2.1 aroma ...

Decision tree is a basic classification and regression method 1, Basic concepts of decision tree The nodes and directed edges of the decision tree are represented respectively: An internal node represents a feature or attributeLeaf nodes represent a classification.A directed edge represents a partition rule The directed edge of the decisio ...

Return to actual house price forecast Use what you have learned to solve real-world problems. Let's use these principles to estimate house prices. Housing valuation is one of the most classic cases to understand regression analysis, which is usually a good entry point. It is in line with people's intuition and with people Our lives are clos ...

catalogue 1, Definitions and formulas 1. Decision tree 2. Anomaly Detection 3. Principal component analysis PCA 2, Code practice 1. Decision tree: Iris iris Iris data classification 2. Anomaly detection 2.1} visualize the probability density function of Gaussian distribution 2.2} establish a model to realize the prediction of abnormal ...

Iris data classification using decision tree algorithm (learning notes) Introduction to decision tree algorithm The process of building a tree Starting from the root node, calculate the information gain (information gain ratio and Gini coefficient) of all eigenvalues, and select the feature with the largest calculation result as the root ...

  1, Introduction 1.1 concept As shown in the figure, it is a decision tree. The square represents the decision block and the ellipse represents the terminating block, indicating that the conclusion has been reached and the operation can be terminated. The left and right arrow branch es are led out from the judgment module, which c ...

1. General 1.1 how does the decision tree work? Decision Tree is a nonparametric supervised learning method. It can summarize decision rules from a series of characteristic and labeled data, and present these rules with the structure of tree view to solve the problems of classification and regression. Decision Tree algorithm is easy to unders ...

preface Decision tree is a common algorithm in machine learning. Relevant mathematical theories I also wrote in the column of mathematical modeling Mathematical modeling learning notes (XXV) decision tree Yes, this blog post does not pay attention to the relevant mathematical principles, but mainly focuses on the effect of using sklearn to re ...