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.

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.

Convolutional Neural Network with Julia: Flux

Abstract

Here, I'll make a convolutional neural network model by Flux with Julia. In the article, Deep learning with Julia: introduction to Flux, I made simple neural network with Flux. Neural network, especially convolutional neural network, is quite efficient in image classification area. So, this time, I'll make the convolutional neural network model to image classification.

Deep learning with Julia: introduction to Flux

Abstract

On this article, I'll try simple regression and classification with Flux, one of the deep learning packages of Julia.

How to make HTTP server for prediction of machine learning model with Julia

Abstract

On this article, I'll try Julia's HTTP server. Concretely, the goal is to make HTTP server that execute k-means’s prediction. About machine learning task, it is usual to set the learned model to HTTP server and post the data to that. So, as a first step of it on Julia, I'll try it. The package used here is HTTP.jl.
Here, I'll just touch the initial step and won’t follow the good or proper manner. When you make the HTTP server for machine learning task, I strongly recommend that you read the official document after this article.

Image segmentation and compression by K-means

Abstract

On this article, I'll try image segmentation and compression by K-means.
This is shown on the popular book, Pattern Recognition And Machine Learning, as an example of K-means. I've never used K-means with setting the segmentation and compression of images as a main purpose, because it is not practical way. But it looks fun on the book. So, I'll try.

Here, for experiment, the code is written in Julia.

EM algorithm with Initialization by K-means

Abstract

On this article, I'll check the EM algorithm with the initialized values by k-means. In many algorithms, initial values are very important theme. On EM algorithm, with inappropriate initial values, it takes much time for convergence and if the algorithm is naively written, it stops with error because of non-positive-definite.

So, I'll do experiment to check how much the accuracy, the number of iteration and time change with and without initialized values by k-means. Here, I'll just touch EM algorithm about the mixture of Gaussian case.
If there is a mistake or inappropriate points, please let me know by comment.

k-means++: Introduction and small experiment with Julia

Overview

On this article, I'll write about k-means++. To say precisely, I'll explain what k-means++ is and do small experiment with Julia. For the people who have experience of Python, Julia code is easy to read. So, basically no problem.
k-means++ is regarded as the algorithm to give nice initialization for k-means and sometimes can be used in other algorithm like EM algorithm.
If you find a mistake, please let me know on comment.

Probabilistic Clustering with EM algorithm: Algorithm and Visualization with Julia from scratch

Abstract

On this article, I'll write probabilistic clustering by EM algorithm from scratch with Julia. Here, I'll touch only about mixture of Gaussian case.
The outcome of clustering becomes below. To the simple artificial data, it is working.

Introduction to K-medoids: Algorithm and Visualization with Julia from scratch

Abstract

On this article, I'll write K-medoids with Julia from scratch.
Although K-medoids is not so popular algorithm if you compare with K-means, this is simple and strong clustering method like K-means. So, here, as an introduction, I'll show the theory of K-medoids and write it with Julia.
As a goal, I'll make animation like below.

Introduction to K-means: Algorithm and Visualization with Julia from scratch

Abstract

On this article, I'll write K-means with Julia from scratch and show animation to see how the algorithm works.
K-means is very simple unsupervised algorithm for clustering. So, when I start to study new programming language, I always use K-means as the theme for writing from scratch.
The following GIF shows how data points are classified into clusters on the way of algorithm going. Relatively easily, we can write K-means code and plot this kind of animation with Julia.



On this article, I used Julia with version 0.6.2.

Note for speeding up Julia's code

Abstract

On this article, I'll re-write the Julia code, which I wrote before, for speed up.
One of the huge advantages of Julia is its performance, speed. But to make advantage of it, we need to write the code in proper rule. So, here, I’ll re-write the code to more efficient one.

kNN by Julia from scratch

Abstract

On this article, I'll write naive kNN algorithm with Julia. Recently, I started to use Julia and need to practice. kNN is relatively simple algorithm and nice for practice. So, here, I'll write simple kNN with Julia.

Simple note for Julia DataFrame

Overview

On this article, I'll write down the Julia's basic dataframe manipulation.
I just started to use Julia. I want to continue to use this language as DS and ML tool. For that, at first I'll focus on the basic dataframe manipulation.
Here, the version of Julia is 0.6.2.

Try Julia: from install to simple mathematical operation

I started to use Julia. Although more or less one year ago, I touched Julia, it didn't become my daily tool. But these days I frequently hear about Julia in data science field and also the news about version 1.0 hit on me. So, I decided earnestly to tackle with it.
On this article, I'll arrange some information of Julia and show some mathematical and matrix operations.