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This infographic, created using EdrawMax, outlines the pivotal moments in African American history from 1619 to the present. It highlights significant events such as emancipation, key civil rights legislation, and notable achievements that have shaped the social and political landscape. The timeline serves as a visual representation of the struggle for equality and justice, emphasizing the resilience and contributions of African Americans throughout history.
This infographic, designed with EdrawMax, presents a detailed timeline of the evolution of voting rights and citizenship in the U.S. from 1870 to the present. It highlights key legislative milestones, court decisions, and societal changes that have expanded or challenged voting access. The timeline underscores the ongoing struggle for equality and the continuous efforts to secure voting rights for all citizens, reflecting the dynamic nature of democracy in America.
This infographic, created using EdrawMax, highlights the rich cultural heritage and outstanding contributions of African Americans. It covers key areas such as STEM innovations, literature and thought, global influence of music and arts, and historical preservation. The document showcases influential figures and institutions that have played pivotal roles in shaping science, medicine, literature, and public memory, underscoring the integral role of African American contributions to society.
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