last-class-for-inf-0615

inf-0615

• open-set vs closed-set

  • Open-set tries to first say if the input is something related with the classes it saw in the training
  • closed-set tries to fit the input in one of the classes, even if does not make sense (e.g. tries to understand a letter in a digits-classifier)

• Preparing and manipulating the data is more important than the algorithm we choose to use in Machine Learning.

Pipeline steps

• Get Data
  • Always split the training data from validation
  • Use the validation to “test your model”
  • Validação cruzada
• Clean, Prepare & Manipulate Data
  • Inspect the data
  • Inpesct features without anotations
  • Cast discreat features
  • Normalise the data
  • Balance the classes
• Train Model
  • Pick the algorithm
  • Define the parameters
  • Train the model
• Test Data
  • Metrics
  • Compare the result of Train vs Validation
  • Check for Overfitting & Underfitting
• Improve
  • See if you should change the complexity of the model

Random notes

• online learning vs batch learning
• k-fold cross-validation
  • You train k models and check the best or you can use ensemble-learning to use all k models