Classification
1. The
distinct stages of designing a classification model are outlined below:
_
Collect your raw data
_
Clean your data (e.g. outlier removal, missing data removal etc.)
_
Preprocess the data (e.g. normalization, standardization, etc.)
_
Determine the type of problem (i.e. classification or regression)
_
Pick an appropriate classifier (e.g. multilayer perceptron, decision tree,
linear
regression, etc.)
_
Choose some default parameters for the classifier, the choice of classifier
and
parameters constitute your model
2. Pick a training/testing strategy (e.g.
percentage split, cross-validation etc.)
_
Train the classifer using your training/testing strategy
_
Analyse the performance of your model
_ If
your results are unsatisfactory consider altering your model (i.e.
changing
the classifer, its parameters, and/or your training/testing
strategy)
and re- training/testing
_ If
your results are satisfactory validate your model on an unseen set of cleaned
and preprocessed data.