MindMap Gallery Decision tree architecture algorithm programming mind map
This picture is a mind map of decision tree architecture algorithm programming. One picture will help you fully understand the relevant content and help you improve efficiency. Come and give it a try~
Edited at 2023-02-19 16:58:43This article discusses the Easter eggs and homages in Zootopia 2 that you may have discovered. The main content includes: character and archetype Easter eggs, cinematic universe crossover Easter eggs, animal ecology and behavior references, symbol and metaphor Easter eggs, social satire and brand allusions, and emotional storylines and sequel foreshadowing.
[Zootopia Character Relationship Chart] The idealistic rabbit police officer Judy and the cynical fox conman Nick form a charmingly contrasting duo, rising from street hustlers to become Zootopia police officers!
This is a mind map about Deep Analysis of Character Relationships in Zootopia 2, Main content: 1、 Multi-layer network of relationships: interweaving of main lines, branch lines, and hidden interactions, 2、 Motivation for Character Behavior: Active Promoter and Hidden Intendant, 3、 Key points of interaction: logic of conflict, collaboration, and covert support, 4、 Fun Easter eggs: metaphorical details hidden in interactions.
This article discusses the Easter eggs and homages in Zootopia 2 that you may have discovered. The main content includes: character and archetype Easter eggs, cinematic universe crossover Easter eggs, animal ecology and behavior references, symbol and metaphor Easter eggs, social satire and brand allusions, and emotional storylines and sequel foreshadowing.
[Zootopia Character Relationship Chart] The idealistic rabbit police officer Judy and the cynical fox conman Nick form a charmingly contrasting duo, rising from street hustlers to become Zootopia police officers!
This is a mind map about Deep Analysis of Character Relationships in Zootopia 2, Main content: 1、 Multi-layer network of relationships: interweaving of main lines, branch lines, and hidden interactions, 2、 Motivation for Character Behavior: Active Promoter and Hidden Intendant, 3、 Key points of interaction: logic of conflict, collaboration, and covert support, 4、 Fun Easter eggs: metaphorical details hidden in interactions.
Decision tree architecture algorithm programming mind map
Decision tree construction
advantage
The computational complexity is not high
Output is easy to understand
Insensitive to missing intermediate values
Can handle irrelevant feature data
shortcoming
May cause over-matching problem
Applicable data types
Numerical type
Nominal type
question
Which feature of the current data set plays a decisive role in classifying the data?
pseudocode
process
Data collection
any method
Prepare data
The tree construction algorithm only works on nominal data
Numerical data must be discretized
analyze data
any method
After construction, you should check whether the graph is as expected
training algorithm
Construct the data structure of the tree
Test algorithm
Calculate error rate using experience tree
Use algorithms
Can better understand the inner meaning of data
Decision tree algorithm
dichotomy
ID3
This section adopts
shortcoming
Cannot handle numeric data directly (although quantification methods are available)
C4.5
CART
Ch9
information gain
Measuring the content of information using information theory
information gain
Changes in information before and after dividing the data set
Select features with the highest information gain
entropy
expected value of information
The higher the entropy, the more data is mixed
Gini Impurity
Randomly select items from a data set and measure the probability of being misclassified into other groups.
This book does not use
Partition the data set
Data requirements for this example
same length
The last column is the category label
Find the best information gain
Recursively build a decision tree
end condition
Traverse all attributes that divide the data set
or all instances under each branch have the same classification
Drawing dendrograms using Matplotlib annotations
Matplotlib annotations
annotation tool annotations
Built-in support for line drawing tools with arrows
Construct annotation tree
question
Determine the x-axis length
Know how many nodes there are
Determine the y-axis length
Know how many layers a tree has
Test and store classifiers
Testing the Algorithm: Performing Classification Using Decision Trees
Using Algorithms: Storage of Decision Trees
pickle
kNN cannot be persisted
Example: Predicting Contact Lens Type Using Decision Trees
data set
over matching
Crop
Ch9