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Introduction to Statistical Learning (Python) - Home Introduction to Statistical Learning (Python) - Home
  • Install instructions
  • Datasets used in ISLP
    • Auto Data
    • Bike sharing data
    • Boston Data
    • Brain Cancer Data
    • Caravan
    • Sales of Child Car Seats
    • U.S. News and World Report’s College Data
    • Credit Card Balance Data
    • Credit Card Default Data
    • Fund Manager Data
    • Baseball Data
    • Khan Gene Data
    • NCI 60 Data
    • New York Stock Exchange Data
    • Orange Juice Data
    • Portfolio Data
    • Time-to-Publication Data
    • S&P Stock Market Data
    • Violent Crime Rates by US State
    • Mid-Atlantic Wage Data
    • Weekly S&P Stock Market Data
  • Transforms for flexible features
    • Polynomial features
    • Spline features
    • Derived features: using PCA on a subset of columns
  • Tools for regression models
    • Building design matrices with ModelSpec
    • Model selection using ModelSpec
    • ANOVA using ModelSpec
  • Helper functions
    • Clustering
    • Generalized Additive Models
    • Survival Analysis
    • Support Vector Machines
  • Labs
    • Introduction to Python
    • Linear Regression
    • Logistic Regression, LDA, QDA, and KNN
    • Cross-Validation and the Bootstrap
    • Linear Models and Regularization Methods
    • Non-Linear Modeling
    • Tree-Based Methods
    • Support Vector Machines
    • Deep Learning
    • Survival Analysis
    • Unsupervised Learning
    • Multiple Testing
  • Creating IMDB dataset from keras version
  • .rst

Helper functions

Helper functions#

Many of the modules in the ISLP module contain just a few helper functions.

  • Clustering
    • Make a toy dataset
    • Cluster it
    • Plot the dendrogram
  • Generalized Additive Models
    • Make a toy dataset
    • Create a GAM
    • Plot the partial dependence plot for first feature
    • Fixing degrees of freedom
      • Create a new GAM with the correctly fixed terms
  • Survival Analysis
    • Define a cumulative hazard
    • Plot survival function
  • Support Vector Machines
    • Make a toy dataset
    • Fit an SVM classifier
    • Slicing through different features

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ANOVA using ModelSpec

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Clustering

By Jonathan Taylor

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