Maiia Bakhova's Blog (8)

Online Ad selection: Upper Confidence Bound method and Thompson Sampling method

The post is devoted to select the most popular ad to display on a webpage to gather the most clicks. The rate at which the webpage visitors click on an ad is called a conversion rate for the add.

Assume that we have several ads and a place on a webpage to show one of them. We can display them one by one, record all the clicks, analyse the results afterwards and figure the most popular. But an ad display may be pricey. It would be more efficient to estimate rates in real time and to…


Added by Maiia Bakhova on February 28, 2019 at 10:00am — No Comments

San Diego Water Pollution Map by Stations

I have been working with San Diego Water quality data project:


Here are data sets:


Regretfully my complete works do not fit into the blog post (or even a few posts) because of a post…


Added by Maiia Bakhova on November 6, 2018 at 11:46am — No Comments

Neural Networks as a Corporation Chain of Command

Neural networks are considered complicated and they are always explained using neurons and a brain function. But we do not need to learn how to brain works to understand Neural networks structure and how they operate. We can look as something people encounter in everyday life more often, like a corporation hierarchy.…


Added by Maiia Bakhova on June 26, 2017 at 10:00am — 2 Comments

Detection of Practical Dependency of Variables with Confidence Intervals

This is an article which attempts to detect dependable variables with non-linear method.

I'm going to apply a method for checking variable dependency which was introduced in my previous post. Because the "dependency" I get with this rule is not true dependency as defined in Probability then I will call variables practically dependent at a confidence level…


Added by Maiia Bakhova on November 2, 2016 at 11:30am — No Comments

Measuring Dependence of Variables with Confidence Intervals.

In this post I will sometimes use a term “variable” for “feature”(“predictor”“) or”outcome“(”predicted value“”).

The question of variable dependencies for a particular data is quite important, because it can help to reduce an amount of predictors used for a model. Or it can tell us what feature is not helpful for a model construction, although it still can be used for engineering of another predictor. For example sometimes it is better to compute speed than to use distance values. In…


Added by Maiia Bakhova on September 6, 2016 at 1:07pm — No Comments

Visualizing Bagged Trees as Approximating Borders

The bagged trees algorithm is a commonly used classification method. By resampling our data and creating trees for the resampled data, we can get an aggregated vote of classification prediction. In this blog post I will demonstrate how bagged trees work visualizing each step.…


Added by Maiia Bakhova on May 18, 2016 at 2:12pm — No Comments

Improving performance of random forests for a particular value of outcome by adding chosen features

Choosing features to improve a performance of a particular algorithm is a difficult question. Currently here is PCA, which is difficult to understand (although it can be used out-of-the-box), requires centralizing and scaling of features and is not easy to interpret. In addition, it does not allows to improve prediction performance for a particular outcome (if its accuracy is lower than for others or it has a particular importance). My method  enables to use features without preprocessing.…


Added by Maiia Bakhova on May 5, 2016 at 11:30am — No Comments

Choosing features for random forests algorithm

There are many ways to choose features with given data, and it is always a challenge to pick up the ones with which a particular algorithm will work better. Here I will consider data from monitoring performance of physical exercises with wearable accelerometers, for example, wrist bands.

The data for this project come from this source: http://groupware.les.inf.puc-rio.br/har.

In this project, researchers used data from…


Added by Maiia Bakhova on February 18, 2016 at 11:00am — 5 Comments

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