Bootstrapping
This method involves randomly sampling the data with replacement, training the model on the sampled data, and then testing it on the original data. This can be used to get a distribution of the model’s performance, which can be useful for understanding how robust the model is. Bootstrapping is a resampling technique used to estimate the accuracy of a model. It involves randomly selecting a sample of data from the original dataset and then training the model on this sample. The model is then tested on another sample of data that is not used in training. This process is repeated a number of times, and the average accuracy of the model is calculated.
Pattern Evaluation Methods in Data Mining
Pre-requisites: Data Mining
In data mining, pattern evaluation is the process of assessing the quality of discovered patterns. This process is important in order to determine whether the patterns are useful and whether they can be trusted. There are a number of different measures that can be used to evaluate patterns, and the choice of measure will depend on the application.
There are several ways to evaluate pattern mining algorithms: