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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

Tools for regression models

Tools for regression models#

  • Building design matrices with ModelSpec
    • Interactions
    • Ordinal variables
    • Structure of a ModelSpec
      • Feature objects
    • Predicting at new points
    • Using np.ndarray
      • Caveats using np.ndarray
  • Model selection using ModelSpec
    • Forward Selection
  • ANOVA using ModelSpec
    • Grouping variables

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Derived features: using PCA on a subset of columns

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Building design matrices with ModelSpec

By Jonathan Taylor

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