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Key words: Predictive models, Predictive
Modeling, Predictive Power, success and failure, response
model, good vs. bad prediction
The following common misperceptions actually reduce the
power of your predictive algorithms.
Myth1: The higher the probability of 'good vs. bad'
or 'success vs. failure', the better is the discriminating power
of the algorithm
Myth2: Positively correlated variable with the 'good vs.
bad' or 'success vs. failure' is better than the negatively
correlated variable
Myth3: Highest correlated variable is the best variable
to improve the prediction and hence should be used in
the model
Myth4: The model building process benefits better if we
create binned discrete classes for independent
variables
Myth5: The data is all we have to better predict the
consumer behavior
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