In the world of Machine learning quite often we would want to create multiple prediction models, compare them and choose the one that is more likely to give results that satisfy our criteria and requirements.
These criteria can vary, sometimes models which have better overall accuracy are chosen, sometimes models that have least Type I and Type II errors(False Positive and False Negative Rates) are chosen, and in some cases models that return results faster with acceptable level of accuracy are chosen (even if not ideal), and there are more such criteria.
Oracle DV has multiple Machine Learning algorithms implemented out of the box for each kind of prediction/ classification. So users have luxury to create more than one model using these algorithms, or using different fine-tuned parameters to those algorithms or using different input training datasets and then, choose best model out of them. But to choose the best model, we need to compare two models and weigh them against our own criteria.
So how to compare these models? Where can we find the data in Oracle Data Visualization to do this comparison? In our previous blog we have talked about related datasets and model quality details they contain. Here is an example of how to use these related datasets to compare two models based on a criteria: Choose model with least Type II (False Negative Rate) errors. This video explains the process of using these related datasets to compare two models:
These criteria can vary, sometimes models which have better overall accuracy are chosen, sometimes models that have least Type I and Type II errors(False Positive and False Negative Rates) are chosen, and in some cases models that return results faster with acceptable level of accuracy are chosen (even if not ideal), and there are more such criteria.
Oracle DV has multiple Machine Learning algorithms implemented out of the box for each kind of prediction/ classification. So users have luxury to create more than one model using these algorithms, or using different fine-tuned parameters to those algorithms or using different input training datasets and then, choose best model out of them. But to choose the best model, we need to compare two models and weigh them against our own criteria.
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2 comments:
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Thanks for sharingData Mining software service providers
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