Cross validation - The model selection problem Objective...

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Cross validation 1 / 9 The model selection problem Objective Often necessary to consider many different models (e.g., types of classifiers) for a given problem. Sometimes “model” simply means particular setting of hyper-parameters (e.g., k in k -NN, number of nodes in decision tree). Terminology The problem of choosing a good model is called model selection . 2 / 9 Model selection by hold-out validation (Henceforth, use h to denote particular setting of hyper-parameters / model choice.) Hold-out validation Model selection : 1. Randomly split data into three sets: training , validation , and test data. Training Validation Test 2. Train classifier ˆ f h on Training data for different values of h . 3. Compute Validation (“hold-out”) error rate for each ˆ f h : err( ˆ f h , Validation ) . 4. Selection: ˆ h = value of h with lowest Validation error rate. 5. Train classifier ˆ f using ˆ h with Training and Validation data. Model assessment : 6. Finally: estimate true error rate of ˆ f using test data.
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