Evaluation of forecasting,regression,Machine learning and Deep Learning

 Choosing the appropriate forecasting technique to employ is a challenging issue and requires a comprehensive analysis of empirical results. Recent research findings reveal that the performance evaluation of forecasting models depends on the accuracy measures adopted. Some methods indicate superior performance when error based metrics are used, while others perform better when precision values are adopted as accuracy measures. As scholars tend to use a smaller subset of accuracy metrics to assess the performance of forecasting models, there is a need for a concept of multiple accuracy dimensions to assure the robustness of evaluation. 

Regression problems are supervised learning problems in which the response is continuousLinear regression is a technique that is useful for regression problems.Classification problems are supervised learning problems in which the response is categorical.

  Deep Learning techniques over regular data sets, tampered data sets and noisy data sets. First, Deep Learning techniques have been investigated over regular data sets, the experiments showed good results in terms of accuracy and error rate

Machine learning continues to be an increasingly integral component of our lives, whether we’re applying the techniques to research or business problems. Machine learning models ought to be able to give accurate predictions in order to create real value for a given organization.

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