optical character recognition • machine vision (e.g., face detection) • natural-language processing (e.g., spoken language understanding) • market segmentation (e.g.: predict if customer will respond to promotion) • Bioinformatics (e.g., classify proteins according to their function) Categorization Tasks
• Monthly income • Gender • Marital status • No of dependants • How much borrowed? • Account balance at middle of the month • Credit score etc… Identify features
+1 • numeric x-data can be Gaussian normalized (really depends on your data) • binary x-data is (-1 +1) encoded • categorical x-data is 1-of-(C-1) effects-coded ( ex: [0,1] or [1,0] or [-1,-1] ) Prepare features for Neural Network
number of features to be used • Output layer – only one layer. Usually one neuron except for multiclass classification (run away: 0, return money: 1) Introducing “maybe” = 3 neurons (run away: {1,0,0}, maybe: {0,1,0}, return money:{0,0,1}
layer. Rare problems requiring more than two hidden layer. One is usually enough. There are many rule-of-thumb methods: • The number of hidden neurons should be between the size of the input layer and the size of the output layer. • The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. • The number of hidden neurons should be less than twice the size of the input layer.
not able to get good results) • Too many hidden neurons results in over-fitting (model fits well with training data but does not perform well with test data)
to model over-fitting) • Learning rate – how much an updating step influences the current value of the weights (too small – runs for long; too large – can go past optimum weights) • Momentum – used to diminish the fluctuations in weight changes over consecutive iterations Network Configuration Terms
= Output from neural net • Vl = length of data • TP = True Positive – accurately predicted valid for a valid transaction. • FP = False Positive – predicted valid for a fraudulent transaction. • FN = False Negative – predicted fraudulent for a valid transaction.