An insight into AUC objective function from the viewpoint of evaluation

Abstract

This article is to think about the model which is with AUC objective function by some evaluation methods. Also, I can say this is to think about the AUC objective function from the viewpoint of evaluation as the title of the article shows.
On the article, AUC as an objective function: with Julia and Optim.jl package, I made a model with AUC objective function. The predicted score by that was distributed in really narrow area, because AUC objective function is based on the order without caring the distance from explained variable. With some evaluation norms, the model's score seems not nice.
 
About this point, just in case, I'll leave the simple experiment.

Julia: version 0.6.3

AUC as an objective function: with Julia and Optim.jl package

Abstract

On this article, I'll do AUC optimization on logistic regression. With Julia's Optim package, relatively easily, we can optimize AUC objective function.

Julia: Version 0.6.3

Follow simple analysis workflow with Julia

Abstract

On this article, with Julia I'll roughly reproduce the simple analysis I did on Simple analysis workflow to data about default of credit card clients.
After I wrote that article, I thought to write the following ones. But, I want to follow the same flow with Julia at first. So, I'll do.

Simple analysis workflow to data about default of credit card clients

Abstract

These days I had opportunity of reading some papers about finance data analysis, meaning credit score, default rate and so on. Personally, I want to tackle with cutting-edge way as soon as possible. But, it is important to see from basic flow on this kind of case. So, here, on this article, I'll follow the basic work flow like univariate analytics with Logistic Regression.
To focus on basic flow and some characteristics, I'll ignore some manner to the data and modeling.
This article more or less follows the chapter 2 and 3 of the following article.

• https://edoc.ub.uni-muenchen.de/17143/1/Kraus_Anne.pdf

How to write Inception module: understanding and coding with Keras

Abstract

This article covers the basic understanding and coding of Inception module.
GoogLeNet, which is composed by stacking Inception modules, achieved the state-of-the-art in ILSVRC 2014. And probably, many people already touched the models which have the name “Inception” by fine-tuning. Here, on this article, I'll deal with the Inception module.
To write the model, I'll use Keras with Python.
To deepen your knowledge, you can use the following paper.

• https://arxiv.org/abs/1409.4842

Various ways of writing Neural Network with Flux: to write complex model

Abstract

Flux is one of the deep learning packages in Julia. It is flexible and easy to use. But, there are not enough examples to grasp the points, although the official documents and model zoo somehow work. So, here, on this article, I'll write down some types of model and the points where I was caught. I'm still on the phase of exploring Flux by reading the source code and trial-error. So, if you find something strange or mistake, please let me know.
On this article, I'll use Julia version 0.6.2.