n the world of machine learning, we usually look at metrics like accuracy or precision to judge how well a model is doing its job. Statistical significance testing, which you might hear about in other fields, isn't as common in machine learning. That's because ML often works with whole datasets, not small random samples. We care more about how well the model predicts outcomes than making big-picture statements about a whole population. Still, we take other steps, like cross-validation, to make sure our models are doing a good job
n the world of machine learning, we usually look at metrics like accuracy or precision to judge how well a model is doing its job. Statistical significance testing, which you might hear about in other fields, isn't as common in machine learning. That's because ML often works with whole datasets, not small random samples. We care more about how well the model predicts outcomes than making big-picture statements about a whole population. Still, we take other steps, like cross-validation, to make sure our models are doing a good job