Comparing Efficiency of Various Machine Learning Algorithms on Different Datasets

Cilj je usporediti algoritme strojnog učenja koji rješavaju problem klasifikacije na različitim skupovima podatka. Usporedba se temelji na točnosti predviđanja algoritama na njima prije neviđenim podacima. Algoritmi koji se uspoređuju su: Naivni Bayesov klasifikator, Stablo odluke, Najbliži susjed, Linearna diskriminantna analiza, Logistička regresija i Stroj potpornih vektora. Usporedba se provodila nad tri različita skupa podataka. Rezultati usporedbe su pokazali da algoritmi Stablo odluke i Najbliži susjed imaju dobru točnost neovisno o vrsti podataka, dok algoritmi Linearne diskriminantne analize, Logističke regresije i Stroja potpornih vektora imaju dobre i bolje rezultate za specifičnije vrste podatka.The goal is to compare different machine learning algorithms for classification problems using different datasets. The comparison is based on predicting the classes of data not previously seen by the algorithms. Algorithms used in the comparison are: Naive Bayes classifier, Decision tree, Nearest neighbour, Linear discriminant analysis, Logistic regression, Support vector machine. Three different datasets were used for the comparison. Results of the comparison show that accuracies of Nearest neighbour and Decision tree algorithms are good, no matter the data type, while algorithms like Linear discriminant analysis, Logistic regression and Support vector machine have better result for specific data types