Performance Of Regression Research Articles

Price Prediction of Used Cars Using Machine Learning

Abstract: The goal of this study is to develop a model that can anticipate fair used car pricing based on a variety of factors such as vehicle model, year of manufacture, fuel type, Price, Kms Driven . In the used car market, this strategy can benefit vendors, purchasers, and car manufacturers. It can then produce a reasonably accurate price estimate based on the data that users provide. Machine learning and data science are used in the model-building process. The data was taken from classified ads for second hand autos. To attain the maximum accuracy, the researchers used a variety of regression approaches, including linear regression, polynomial regression, support vector regression, decision tree regression, and random forest regression. This project visualized the data to better comprehend the dataset before starting the model-building process. To assure the regression's performance, the dataset was partitioned and changed to fit the regression. R-square was used to evaluate the performance of each regression .The final model contains more elements of used autos than earlier research while also having a higher forecast accuracy. Keywords: Analysis, Machine Learning, Ridge Regression, Lasso Regression, Linear Regression.

Fuzzy Least Squares for Identification of Individual Pharmacokinetic Parameters

In this paper, we examined the added value of fuzzy nonlinear regression to identify individual pharmacokinetic parameters in the case of noisy fuzzy data and/or small sample sizes. We first described three approaches that use least squares of errors as a fitting criterion for parameter estimation by fuzzy regression. Next, we compared the estimation and prediction capability of fuzzy least squares (FLS) and ordinary least squares (OLS) regressions via a simulation experiment, so as to determine the conditions of data size and variability under which one approach could be deemed superior over the other. We considered two empirical pharmacokinetic models. Our results showed that OLS regression outperformed FLS regression when the sample size was larger and/or there existed more outliers in the data. Overall, FLS regression was more powerful as the dataset size decreased. When the data were smaller in size and contained more variability, FLS regression's performance remained better than that of the OLS regression. Although the accuracy of the three FLS regression approaches was very close in almost all instances, those that estimated fuzzy parameters were superior in terms of predictive capability. These findings could aid in selecting the proper regression technique to employ in the presence of fuzzy data.

Evaluation of the Stratified Regression Estimator for Auditing Accounting Populations

In this paper, we compare the performance of (stratified) regression with that of other statistical estimators advocated for use in auditing contexts. A need for this study was suggested by Neter and Loebbecke [1975, p. 140] and it uses their actual accounting populations as a frame. As such, our study constitutes a direct extension of their research. The first section of the paper describes the background, while the second discusses the theoretical relationship of regression to other estimators. The third section describes an empirical study which indicates that the theoretical advantages of regression may not be realized in practice, if the test populations are representative. We also evaluate various rules of thumb concerning the minimum number of book value errors encountered in a sample (see Roberts [1978, p. 74]). These heuristics do indeed appear to improve regression's performance, but limit the accounting populations to which the technique is applicable.