The Importance of Testing a Hypothesis Before Building Machine Learning Models in Healthcare:
When it comes to machine learning in healthcare, one of the most important things to do is test your hypothesis before building your models. Failing to test your hypothesis increases the risk of bias creeping into your models. Today, we’ll examine how to test your hypothesis and why it’s essential.
What is hypothesis testing in machine learning
Hypothesis testing is a statistical method used to assess a hypothesis’s validity or evaluate two mutually exclusive statements. The hypothesis being tested is typically about the value of a population parameter, such as the mean or variance. In machine learning, hypothesis testing can be used to assess the performance of a model. For example, a healthcare provider may use hypothesis testing to compare the accuracy of two models for predicting heart disease. In this case, the null hypothesis would be that there is no difference between the two models. Suppose the results of the hypothesis test are statistically significant. In that case, the null hypothesis can be rejected in favor of the alternative hypothesis that there is a difference between the two models.
