In another instance, India’s Central Statistics Organization (CSO) changes its method of computation of national income due to the overestimation in the growth rates. In finding the optimal price to maximize profit/revenue with statistical and machine learning models, when the size of available products increases, the predicted gross profit tends to overestimate. For example, as a social and business science, price science uses economics, statistics, econometrics, and mathematical models to study the problem of setting prices. Over or under-estimation is a real challenge in many data science applications, from business to life science, to derive a reliable prediction generated by machine learning models. The regularization terms aim to control the prediction variability with a slight increase in bias.
Model complexity in regression learning models is displayed in high prediction variability.
Specifically, regression regularization is an established method of increasing prediction accuracy in many regression models. Model regularization is a simple yet efficient way to compute model parameters in the presence of constraints to control model complexity. In modern machine learning, regularization is a common practice to control the ability of a model to generalize to new settings by trading off the model’s complexity. The main focus of machine and statistical learning models is on developing reliable predictive models based on available data.
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