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Random forest construction
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Random forest construction
by Dávid Natingga
Data Science Algorithms in a Week
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
Classification Using K Nearest Neighbors
Mary and her temperature preferences
Implementation of k-nearest neighbors algorithm
Map of Italy example - choosing the value of k
House ownership - data rescaling
Text classification - using non-Euclidean distances
Text classification - k-NN in higher-dimensions
Summary
Problems
Naive Bayes
Medical test - basic application of Bayes' theorem
Proof of Bayes' theorem and its extension
Extended Bayes' theorem
Playing chess - independent events
Implementation of naive Bayes classifier
Playing chess - dependent events
Gender classification - Bayes for continuous random variables
Summary
Problems
Decision Trees
Swim preference - representing data with decision tree
Information theory
Information entropy
Coin flipping
Definition of information entropy
Information gain
Swim preference - information gain calculation
ID3 algorithm - decision tree construction
Swim preference - decision tree construction by ID3 algorithm
Implementation
Classifying with a decision tree
Classifying a data sample with the swimming preference decision tree
Playing chess - analysis with decision tree
Going shopping - dealing with data inconsistency
Summary
Problems
Random Forest
Overview of random forest algorithm
Overview of random forest construction
Swim preference - analysis with random forest
Random forest construction
Construction of random decision tree number 0
Construction of random decision tree number 1
Classification with random forest
Implementation of random forest algorithm
Playing chess example
Random forest construction
Construction of a random decision tree number 0:
Construction of a random decision tree number 1, 2, 3
Going shopping - overcoming data inconsistency with randomness and measuring the level of confidence
Summary
Problems
Clustering into K Clusters
Household incomes - clustering into k clusters
K-means clustering algorithm
Picking the initial k-centroids
Computing a centroid of a given cluster
k-means clustering algorithm on household income example
Gender classification - clustering to classify
Implementation of the k-means clustering algorithm
Input data from gender classification
Program output for gender classification data
House ownership – choosing the number of clusters
Document clustering – understanding the number of clusters k in a semantic context
Summary
Problems
Regression
Fahrenheit and Celsius conversion - linear regression on perfect data
Weight prediction from height - linear regression on real-world data
Gradient descent algorithm and its implementation
Gradient descent algorithm
Visualization - comparison of models by R and gradient descent algorithm
Flight time duration prediction from distance
Ballistic flight analysis – non-linear model
Summary
Problems
Time Series Analysis
Business profit - analysis of the trend
Electronics shop's sales - analysis of seasonality
Analyzing trends using R
Analyzing seasonality
Conclusion
Summary
Problems
Statistics
Basic concepts
Bayesian Inference
Distributions
Normal distribution
Cross-validation
K-fold cross-validation
A/B Testing
R Reference
Introduction
R Hello World example
Comments
Data types
Integer
Numeric
String
List and vector
Data frame
Linear regression
Python Reference
Introduction
Python Hello World example
Comments
Data types
Int
Float
String
Tuple
List
Set
Dictionary
Flow control
For loop
For loop on range
For loop on list
Break and continue
Functions
Program arguments
Reading and writing the file
Glossary of Algorithms and Methods in Data Science
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Playing chess example
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Construction of a random decision tree number 0:
Random forest construction
We construct a random forest that will consist of four random decision trees.
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