|
| |
|
| |
|
|
Statistical models for prediction of project duration
Oberta, Dušan ; Žák, Libor (referee) ; Hübnerová, Zuzana (advisor)
Cieľom tejto bakalárskej práce je odvodiť štatistické modely vhodné pre analýzu dát a aplikovať ich na analýzu reálnych dát týkajúcich sa časovej náročnosti projektov v závislosti na charakteristikách projektov. V úvodnej kapitole sú študované lineárne regresné modely založené na metóde najmenších štvorcov, vrátane ich vlastností a predikčných intervalov. Nasleduje kapitola zaoberajúca sa problematikou zobecnených lineárnych modelov založených na metóde maximálnej vierohodnosti, ich vlastností a zostavením asymptotických konfidenčných intervalov pre stredné hodnoty. Ďalšia kapitola sa zaoberá problematikou regresných stromov, kde sú znova ukázané metóda najmenších štvrocov a metóda maximálnej vierohodnosti. Boli ukázané základné princípy orezávania regresných stromov a odvodenie konfidenčných intervalov pre stredné hodnoty. Metóda maximálnej vierohodnosti pre regresné stromy a odvodenie konfidenčných intervalov boli z podstatnej časti vlastným odvodením autora. Posledným študovaným modelom sú náhodné lesy, vrátane ich základných vlastností a konfidenčných intervalov pre stredné hodnoty. V týchto kapitolách boli taktiež ukázané metódy posúdenia kvality modelu, výberu optimálneho podmodelu, poprípade určenia optimálnych hodnôt rôznych parametrov. Na záver sú dané modely a algoritmy implementované v jazyku Python a aplikované na reálne dáta.
|
|
|
Ensemble learning methods for scoring models development
Nožička, Michal ; Witzany, Jiří (advisor) ; Cipra, Tomáš (referee)
Credit scoring is very important process in banking industry during which each potential or current client is assigned credit score that in certain way expresses client's probability of default, i.e. failing to meet his or her obligations on time or in full amount. This is a cornerstone of credit risk management in banking industry. Traditionally, statistical models (such as logistic regression model) are used for credit scoring in practice. Despite many advantages of such approach, recent research shows many alternatives that are in some ways superior to those traditional models. This master thesis is focused on introducing ensemble learning models (in particular constructed by using bagging, boosting and stacking algorithms) with various base models (in particular logistic regression, random forest, support vector machines and artificial neural network) as possible alternatives and challengers to traditional statistical models used for credit scoring and compares their advantages and disadvantages. Accuracy and predictive power of those scoring models is examined using standard measures of accuracy and predictive power in credit scoring field (in particular GINI coefficient and LIFT coefficient) on a real world dataset and obtained results are presented. The main result of this comparative study is that...
|
|
| |
|
| |
|
| |
|
|
Classification and Regression Forests
Klaschka, Jan ; Kotrč, Emil
Classification forest is a classification model constructed by combinaning several classification trees. A predictor vector is assigned a class by each of the trees, and the overall classification function is given by majority voting. Similarly, a regression forest consists of several regression trees, and the overall regression function is defined as a weighted average of regression functions of individual trees. Brief explanations of some forest construction methods, namely of bagging, boosting, arcing and Random Forests, are given.
|
|
|
Classification and Regression Forests.
Klaschka, Jan ; Kotrč, Emil
Classification forest is a classification model constructed by combinaning several classification trees. A predictor vector is assigned a class by each of the trees, and the overall classification function is given by majority voting. Similarly, a regression forest consists of several regression trees, and the overall regression function is defined as a weighted average of regression functions of individual trees. Brief explanations of some forest construction methods, namely of bagging, boosting, arcing and Random Forests, are given.
|
guest ::
