Tutorials

1

            Overview of Decision Trees
   What are Decision Trees ?
   Growing the Tree.
   Choosing a predictor for a split.
   How big a Tree ?
   Using the Tree.

2

                Different types of predictors
   Categorical predictors :
       * Merging the modelities ?
       * Special case : binary dependent variable.
   Ordinal predictors : merging and splitting.
   Numerical predictors.

Tutorials

3

                                     Splitting nodes
   Impurity criterion :
      * Misclassification rate.
      * Gini index.
      * Entropy.
      * Variance.
   Chi-square based splitting : CHAID
   The "Twoing" split.
   Priors, weights and costs.

 4

                                    The "right size" Tree
    Tree overgrowing.
    Stopping the growth.
    Pruning a large Tree.

Tutorials

1

            Overview of Discriminant Analysis
    Discriminant Factor Analysis.
    Building a classifier.
    Assumptions on classes.
    Choosing the best set of predictors.

2

                Discriminant Factor Analysis
   Which variables discriminate between classes ?
   Creating discriminant dunctions.
   Is the model trustworthy ?
   Predictor selection.
   Categorical variables.

Tutorials

3

                                     The classifier
   Geometric discriminant function.
   Probabilistic discriminant functions.
   Building the classifier.
   Is the model trustworthy ?
   Predictor selection.
   Categorical variables.

 4

                                    Complements
   Mahalanobis distance. Metrics.
   Mahalanobis distance criterion.
   Decomposition of the total covariance matrix.
   Variance of a projection on a line.

Tutorial

1

Fisher's criterion J.
   Fisher's vector.
   Is Fisher's discriminant optimal ?

2

Mathematical complements :
       * Derivative of a quadratic form.
       * Finding the maximum of the ratio of two quadratic forms.

Interactive Animation

Classes can be dragged around.
    Line can be spinned to Fisher's position.

Tutorial

1

Intuitive approach to Logistic Regression :
       * Regression on class indicators.
       * The logistic function.
   From Discriminant Analysis to Logistic Regression :
       * The logit function.
       * Direct calculation of posterior probabilities.

Tutorial

2

Estimating the parameters :
      * Likelihood maximization.
      * Non-linear optimization.
      * Local minima.
      * Special case : classes are separable.