An Introduction to Statistical Learning

April 21, 2020 - 3 minute read -
academic

The purpose of this series of posts is to create a concise overview of important modelling concepts, and the intended audience is someone who has learned these concepts before, but would like a refresher on the most important bits (e.g. myself).

I. Motivation

Statistical learning involves building models to understand data.

Why estimate the function, f, that connects the input and output?

  1. Prediction:
    • estimate output with a black box function to minimize the reducible error
  2. Inference:
    • which predictors are associated with the response?
    • what is the relationship between te response and each predictor?
    • can the relationship between Y and each predictor be adequately summaried using linear equation?

How do we estimate f?

  1. Parametric Methods make an assumption about functional form (i.e. linear), then use training data to fit or train the model.
    • Pro: Simplifies the problem down to estimating a set of parameters, and results are easily interpretable
    • Con: The chosen model will likely not match the unknown form of f
  2. Non-parametric Methods do not make explicit assumptions about the functional form of f.
    • Pro: Potential to accurately fit a wider range of possible shapes
    • Con: Need a very large number of observations to obtain an accurate estimate.

What are the two types of statistical learning?

  1. Supervised learning has predictor measurements and associated response measurements.
  2. Unsupervised learning as observed measurements, but no associated response; often seek to understand relationships between variables or between observations.
    • Note: semi-supervised learning is when there are a limited number of response observations, and these methods are outside the scope of this book)

How do we assess model accuracy?

  • Bias-Variance Tradeoff
    • The goal is to develop a model that balances inflexible methods with large bias/small variance and flexible methods with small bias/large variance:


  • Regression: Mean squared error
  • Classification: Error rate

where is an indicator variable that equals 1 if the prediction is incorrect and 0 if correct.

  • Notes:
    • The Bayes classifier on average minimizes the test error rate by assigning each observation to the most likely class, given its predictor values: .
    • The Bayes decision boundary is the separating boundary between classes (note: K-nearest neighbors often gets very close to the optimal Bayes classifier).
    • The Bayes error rate is the lowest possible test error rate:



II. Methods

Details will be covered in later posts.

  • Regression: Predicting or explaining a continuous (quantitative) output
    • Linear Regression with stepwise selection
    • Ridge Regression
    • Lasso
    • Principle Components Regression
    • Partial Least Squares
    • Non-Linear Additive Models
  • Classification: Predicting or explaining a categorical (qualitative) output
    • Logistic Regression
    • Linear Discriminant Analysis
    • K-Nearest Neighbors
    • Support Vector Machines
  • Resampling Methods: Techniques that produce more accurate models
    • Cross validation
    • Bootstrap
  • Tree-based Methods: Stratifying or segmenting the predcitor space into regions
    • Bagging
    • Boosting
    • Random Forests
  • Clustering: Grouping individuals according to observed characteristics
    • Principle Components Analysis
    • K-means Clustering
    • Hierarchical Clustering
←Index