Publications by Mustafa Arslan
Model Evaluation 1
Introduction Performance evaluation is an important part of machine learning process. We need to know how well the model works on the unseen data set. ROC curve along with other methods is one of the most important evaluation metrics for checking any classification model’s performance. ROC curves do not only tell us how well the model works, b...
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Model Evaluation 2
Introduction Performance evaluation is an important part of machine learning process. We need to know how well the model works on the unseen data set. In this study, I will evaluate my model based on the following Classification metrics: 1. Accuracy Score: How well the model accurately predict the actual class 2. Missclassification Score:1- Acc...
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Model Evaluation 3
Introduction F1 Score In statistical analysis of binary classification, the F-score or F-measure is a measure of a test’s accuracy. It is calculated from the precision and recall of the test. Recall: The recall is the number of true positive results divided by the number of all samples that should have been identified as positive. \[ Recall =...
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Categorical Data Analysis 1
Introduction A two-way contingency table is a cross-classification of observations by the levels of two discrete variables. There are 3 important points we can obtain from the contingency tables: - Compare relative frequency of occurrence of some characteristics of the two groups - Are two characteristics independent? - Is one characteristics a ...
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Categorical Data Analysis 2
Introduction ODDS Ratio: This is the perhaps the most commonly used measure of association. We also use Odds ratio for many log-linar and logistic models. If odds equal to 1, “success” and “failure” are equally likely. If odds > 1, then “success” is more likely than “failure”. If odds < 1, then “success” is less likely than ...
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Generalized Linear Models 1
Introduction In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted ...
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Generalized Linear Models 4
Introduction The multinomial logistic regression model is a simple extension of the binomial logistic regression model. Both multinomial and ordinal models are used for categorical outcomes with more than two categories. Ordinal logistic regression is used to predict an ordinal dependent variable given one or more independent variables. It can b...
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Computational Methods 2
Introduction Simulation is an important technique in applied mathematics. It typically means using computers to conduct experiments! In statistics, it is very useful way to check if a statistical model has the necessary properties and this involves sampling from probability distributions. In most real life situations, analytical derivations of s...
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Computational Methods 3
Introduction Accept-Reject simulation method Typically, a C.D.F. will not come up in nice closed expression for us to invert evrytime. There are many distributions for which the inverse transform method and even general transformations will fail to be able to generate the required randomvariables. When we come up against that problem, the next s...
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