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Predictive models are effective tools that can be used in various spheres of individuals’ lives. To ensure excellent outcomes of prediction, it is vital to assess these models. This paper features a discussion of the methods used for evaluation, as well as their benefits and disadvantages. In addition, the report reflects on clustering, the methods it uses, and its applicability to daily life.
Assessment of Predictive Models
Various approaches and methods can be used to assess the performance of predictive models. One of the main ones is explained variation that can be utilized for numerical data and can be implemented as an accuracy evaluation for software and computing programs. This approach involves a correlation coefficient (r) and the coefficient of determination (r2) (Li). One of the primary benefits of this assessment method is that r and r2 are components of mean square error (MSE), which is commonly involved in the measurement of errors. Potential disadvantages of explained variation are that r and r2 were proven to be biased, and r can only be utilized when the pieces of input data are equal (Li).
Another approach to the assessment of predictive models is the Brier score. This method is considered superior compared to others as it utilizes the concept of a proper scoring rule, which means that the metric is maximized with the utilization of correct probabilities (Assel et al.). The benefit of this approach is that it is affected by calibration and discrimination at the same time. Moreover, the Brier score can estimate the mean squared distance between the observed results and the expected ones (Assel et al.). The drawback of this method is that it is highly dependent on the prevalence and may show poor results in cases, in which prevalence is low.
Clustering can be explained as grouping data points based on their similar features or resemblance. It may be used in many spheres, such as information technology (IT), marketing, biology, and urban architecture. The aim of clustering is to divide the pieces of unlabeled information into homogeneous groups, which can be used later for scientific or other purposes (Priy). Clustering may work in various ways by using different approaches to grouping; they include density- and hierarchy-based ones, as well as partitioning and grid-based methods. The example of the simplest algorithm is the definition of centroids for each potential cluster and the association of all data points with the nearest centroids.
I can use clustering in my daily life in various ways. One of the examples is a 5-day trip to a different city, where I want to visit 30 places that include several museums, parks, art galleries, and restaurants. In this case, I need to divide these places into buckets for each day of my trip. To solve this problem, it is necessary to define a similar trait between these elements, which can be located. Then, I can divide the results into five groups based on the duration of the trip. This way, every day, I can travel to a particular part of the city and explore the places that are close to it. In this case, clustering allows me to save time and organize my trip effectively.
Explained variation and the Brier score are two of the methods that can be used for the assessment of predictive models. Both of them have strong and weak points and should be selected based on expected outcomes. Clustering is a method of organization of similar pieces of information. It can be utilized in various spheres and can be effective when applied to daily life.
Assel, Melissa, et al. “The Brier Score Does Not Evaluate the Clinical Utility of Diagnostic Tests or Prediction Models.” Diagnostic and Prognostic Research, vol. 1, no. 19, 2017, Web.
Li, Jin. “Assessing the Accuracy of Predictive Models for Numerical Data: Not r Nor r2, Why Not? Then What?.” PloS One, vol. 12, no. 8, 2017, Web.
Priy, Surya. “Clustering in Machine Learning.” GeeksForGeeks, 2019. Web.