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Bias Algorithm
Thu Jul 23, 2015 11:00 pm
Algorithm bias: A statistical review
The most general (and toughest) challenge faced by technical analysts is neither optimization (optimizing parameters is straightforward) nor overfitting (overfitting avoidance is an assumption), but how best to use domain knowledge to infer an appropriate bias in their algorithms.
At the risk of oversimplifying, statistics generally concerns testing a given hypothesis, while machine learning concerns formulating the process of generalization as a search through possible hypotheses in an attempt to find the best hypothesis.
Classical statistics involves calculating the probability of the data if the null hypothesis is true, while Bayesian inference involves calculating the probability of a hypothesis, given the data.
Read more
The most general (and toughest) challenge faced by technical analysts is neither optimization (optimizing parameters is straightforward) nor overfitting (overfitting avoidance is an assumption), but how best to use domain knowledge to infer an appropriate bias in their algorithms.
At the risk of oversimplifying, statistics generally concerns testing a given hypothesis, while machine learning concerns formulating the process of generalization as a search through possible hypotheses in an attempt to find the best hypothesis.
Classical statistics involves calculating the probability of the data if the null hypothesis is true, while Bayesian inference involves calculating the probability of a hypothesis, given the data.
Read more
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