Founder and Managing Principal of DBP Institute. I help companies transform technology and data into a valuable business asset. Read Prashanth Southekal's full executive profile here.
The Heisenberg uncertainty principle, which has origins in physics,"states that there is a limit to the precision with which certain pairs of physical properties of a particle, such as position and momentum, can be simultaneously known." In simple terms, the uncertainty principle states that when you try to simultaneously measure certain variables of an entity, you cannot measure all the variables reliably. Often known as the curse of dimensionality, i.e.
2. Minimize the influence of environmental conditions, observer bias, and entity behavior on measurements with proper controls and processes. 1. As large sample sizes provide more accurate estimates of the population, the sample data selected should be the largest size to get the required statistical precision.
What is the solution to address model uncertainty? How can one address model drift and balance bias and variance? The primary reason for model drift, bias, and variances is the change in business processes.
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