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After the process of segregating and compiling data and setting up the problem, we are supposed to apply the necessary algorithms and tools to solve the problem. How do we do that?
Machine Learning provides the answers to that. However, before getting the answers a whole lot of time has to be invested in choosing the right algorithm to get effective and speedy answers to tedious problems.
More than one metric should be tested because one may give desirable results while the other may sabotage the model with its findings. Thus, theoretical knowledge will always help evaluate the model better. A machine will always produce an outcome and we have no idea it is the correct one or not unless someone hints that out in our model. However, a thoughtful evaluation may help us catch the problem and avoid making silly mistakes with grave results. Evaluation is thus important we just cannot do away with. It is better to avoid the shadow of a doubt when the theory of Relativism works so strongly when we deal with real-life data which needs possible human intervention to be analyzed so that machines do not give a purely mechanical answer.
It is always better to choose a set pattern to evaluate the problem so that you do not deviate from any important concerns. Test Harness is a spot-checking measure that gives a fair measure of the worthiness of the data set. Further involved in the selection of test and training datasets and various measures of performance to make the problem meaningful and insightful.
Precision and Recall are the three main metrics used to evaluate models of classification. In cases the data is uneven, precision and recall are more useful. If those metrics are combined, the f-score is evaluated. A greater F- score is always preferred in case of the same number of independent variables in the model.
Metrics, however, are not the most effective and we have the possibility and availability of better measures to do the same. They might give us a false sense of hope and an incorrect idea of accuracy.
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