Supervised classification methods are applied in many fields. The main problem of applying these methods is how to select the most appropriate method in particular case. The literary review was fulfilled and the advantages and disadvantages of mostly used criterion of supervised classification methods comparisons were ascertained. Then the methodology of comparisons was suggested. The analysis of SAS system procedures and macro commands was made. It was ascertained that there is not comfortable software which allows comparing the results of supervised classification methods. This work demands a lot of work, good knowledge of SAS programming language and high qualification in programming. So, the main purpose of this work is to expand the statistical data analysis system SAS possibilities in comparison of supervised classification methods and classificate various data. In this work the possibilities of SAS system are expanded by the tool which allows comparing quality of the linear, quadratic, kernel, nearest neighbor’s discriminant analysis and logistic regression analysis methods. There were used classification error estimates which were got by resubstition, cross–validation leave one out, bootstrap and Monte Carl cross–validation methods, although classification error confidence intervals which were got by non-parametric bootstrap method. The test of created tool was made with various data (different sample sizes, various classis separability, violations of assumptions... [to full text]

In this project, we explored the usage of machine learning in classifying portable electronic devices. The primary objective was to identify devices such as laptops, smartphones, and tablets, based on their physical and technical specification. These specifications, sourced from the Pricerunner price comparison website, contain height, Wi-Fi standard, and screen resolution. We aggregated this information into a dataset and split it into a training set and a testing set. To achieve the classification of devices, we trained four popular machine learning models: Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbor (kNN), and Fully Connected Network (FCN). We then compared the performance of these models. The evaluation metrics used to compare performance included precision, recall, F1-score, accuracy, and training time. The RF model achieved the highest overall accuracy of 95.4% on the original dataset. The FCN, applied to a dataset processed with standardization followed by Principal Component Analysis (PCA), reached an accuracy of 92.7%, the best within this specific subset. LR excelled in a few class-specific metrics, while kNN performed notably well relative to its training time. The RF model was the clear winner on the original dataset, while the kNN model was a strong contender on the PCA-processed dataset due to its significantly faster training time compared to the FCN. In conclusion, the RF was the best-performing model on the original dataset, the FCN showed impressive results on the standardized and PCA-processed dataset, and the kNN model, with its highest macro precision and rapid training time, also demonstrated competitive performance.