Testing A Model

Once the training phase is completed, the next phase entered is the testing phase. In this phase, the testing data (new samples of data which is not present in the training set) is tested to check for an output, i.e., this is the phase where the algorithm chosen is actually run on the test data. The algorithm chosen is run on the test data and an output is obtained. For instance, if the input is a sound sample that needs to be classified as human or dog, the algorithm uses the learned parameters from training set in order to identify the given test sample.

For this, we need to select a classifier appropriate to the application on hand. Different classification algorithms yield varying performances for different applications. For example, the a classifier may be good for text processing but not for audio processing. A few classifiers may be identified and each of them may be tried and evaluated by their accuracy in identifying the test data correctly and the one with the best results may be used. The testing phase is dependent on the training phase for its performance. For example, care must be taken to ensure that the samples in the training set contain sufficiently clear and relevant data (in case of speech processing, pure samples of speech with the required words, less distortion and background noise).

The testing phase also determines the performance of the model.