D5 kiran BD - discussion PDF

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discussion tree which is generally identification which is used for the models where they can collect the records of the credit their calls which can be based on their training the applied for previously which are not seen records were they can validate the accuracy for their reasons which can actually apply their previously non seen records for the training and the tests which are set and will be used.

There are generally these attributes which are split and they can complete this decision tree which is usually approachable and their greedy algorithm which is top-down and these decision trees which can be based on representation of their classes where the homogenous effects which are preferred and their techniques which will be based on the branches for the removed prunes and their decision tree. there are several ways which can help these separate sets for their training and their advantages for their evaluation basis of the accuracy suppose consider the main drawbacks for the data sets can be based on the mispredictions which come from the data sets and they can be fitted accordingly to the sets for the under fittings based over fittings and this can become a problem and their identity gets reduced based on their estimating methods where they can also set a different examples for the data sets. reference Michael Steinbach, P.-N. T. (2016). Introduction To Data Mining. Pearson India....