The Examples Book
Appendices
Corporate Partners National Data Mine Network Personal Programming Languages Projects Registration Seminar TA Training Starter Guides Template Module The Data Mine Workshops The Examples Book University Partners

Starter Guides

    • Introduction
    • Data Science
      • Data Modeling Process
        • Introduction
        • General Principles
          • The Meaning of f(x) in Data Science
          • Bias Variance Tradeoff
          • Curse Of Dimensionality
          • Frequentism and Bayesianism
        • Resampling Methods
          • Cross Validation
            • Train, Valid and Test Splits
            • Leave-One-Out CV & k-Fold CV
        • Modeling Process
          • Choosing a Model
            • Flexibility vs. Interpretability
            • Prediction vs. Inference
            • Classification vs. Regression
            • Parameterization
            • Supervision
          • Step By Step Process
            • Data Wrangling
            • EDA: Exploratory Data Analysis
            • Thinking About The Output
            • Building Data Pipelines
            • Preprocessing
            • Training/Fitting Your Model
            • Measuring Model Fit
            • Model Deployment
      • Data Analysis Techniques
        • Introduction
        • Natural Language Processing(NLP)
          • NLTK (Natural Language Toolkit)
          • Sentiment Analysis
          • Chatbots
          • Prodigy
            • Deploy and Access
          • Word Embeddings
            • Protein Sequence Embedding
          • Transformers
        • GIS
          • GIS Basic Example
          • ArcGIS
          • Basic PostGIS
          • Advanced PostGIS
          • Map Matching
          • Route Optimization
        • Neural Networks/Deep Learning
          • Recurrent Neural Networks
          • Convolutional Neural Networks
        • Computer Vision
        • Clustering
        • Trees
        • Naive Bayes
        • k-Nearest Neighbors
      • Data Visualization
        • Dashboarding Tools
          • PowerBI
          • Tableau
        • With Python
          • Plotly (TDM Seminar 20200)
          • Dashboarding (TDM Seminar 40200)
          • Matplotlib, Plotly (Programming Languages Book)
        • With R
          • ggplot (TDM Seminar 20200)
          • Base graphics, ggplot (Programming Languages Book)
      • Gathering Data
        • Web Scraping
          • Scraping With Python
        • Free Data Sets/Sources
    • Data Engineering
      • Databases
        • SQL
          • SQLite
      • Containerization and Kubernetes
        • Containers
        • Using Data Mine Containers
          • Container Tag Library
        • Kubernetes
        • Deploy Your First Application
        • PySpark
      • SLURM (Simple Linux Utility for Resource Management)
        • SLURM Jobs on Anvil
      • FastAPI Example
    • Programming Languages for Data Professionals
      • Python
        • Introduction (Programming Languages Book)
        • Introduction, pandas, classes, data wrangling (TDM Seminar 10200)
        • Web scraping, plotting (TDM Seminar 20200)
        • Testing, kernels, packages and API’s (TDM Seminar 30100)
      • R
        • Introduction (Programming Languages Book)
        • Introduction, Basics, Apply functions (TDM Seminar 10100)
        • Plotting, tidyverse (TDM Seminar 20200)
      • SQL
        • Introduction (Programming Languages Book)
        • SQL (TDM Seminar 20100)
        • SQLite (TDM Seminar 40100)
      • Perl (good general purpose language to know)
        • Perl (Programming Languages Book)
    • Tools, Standards & More
      • Jupyter
      • Data Ethics
      • The Bookshelf
      • Data Formats
        • HTML
        • JSON
        • XML
      • Matlab
        • Training
      • git
        • Github Desktop
        • Workflows
        • Setup git on Anvil
        • git CLI on Anvil
        • git Terminology
      • Unix
        • Shells
        • Files
        • Jupyter Lab Kernels
        • Unix Kernels
        • awk Overview
        • bash Overview
        • Regular Expressions
        • Standard Utilities
          • man
          • pwd
          • ls
          • cd
          • type
          • which
          • touch
          • cp
          • mv
          • mkdir
          • rm
          • rmdir
          • head
          • less
          • tail
          • cat
          • diff
          • df
          • du
          • stat
          • wc
          • find
          • cut
          • sort
          • uniq
          • tr
          • sed
          • grep
          • awk
          • chmod
          • chown
          • rsync
          • ssh
          • scp
        • Text Editors
          • vim
          • emacs
          • nano
          • Sublime Text
          • VSCode
        • Other Topics
          • ~ & . & ..
          • Piping
          • Redirection
          • Environment Variables
          • Scripts
          • Permissions
          • Cron
          • systemd
          • Setting Up VMs
      • Windows Environments
        • Connecting to a Windows Server
    • Anvil
      • What is RCAC?
      • ACCESS Setup
        • Purdue User Setup
        • General User Setup
        • Other User Setup
        • ACCESS Email Update
        • Helpful ACCESS Links
      • Setup git on Anvil
      • Push Code to Github From Anvil
      • Uploading Data
      • Getting Started with Anvil
      • Setting up a Windows VM on Anvil
      • Using GPU’s on Anvil
  • Corporate Partners
    • stable
  • National Data Mine Network
    • stable
  • Personal
    • stable
  • Programming Languages
    • stable
  • Projects
    • stable
  • Registration
    • stable
  • Seminar TA Training
    • stable
  • Starter Guides
    • stable
  • Template Module
    • stable
  • The Data Mine Workshops
    • stable
  • The Examples Book
    • stable
  • University Partners
    • stable
Starter Guides stable
  • Starter Guides
  • Data Science
  • Data Modeling Process
  • Resampling Methods
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Resampling Methods

Resampling refers to the creation of new samples based on observed samples. The primary methods of this include cross validation and bootstrapping.

Frequentism and Bayesianism Train, Valid and Test Splits

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