XGBoost: theory and practice

Understand how one of the most popular algorithms works and how to use it

Introduction

XGBoost stands for eXtreme Gradient Boosting and it’s an open-source implementation of the gradient boosted trees algorithm. It has been one of the most popular machine learning techniques in Kaggle competitions, due to its prediction power and ease of use. It is a supervised learning algorithm that can be used for **regression **or **classification **tasks.

Regardless of its futuristic name, it’s actually not that hard to understand, as long as we first go through a few concepts: decision trees and gradient boosting. If you are already familiar with those, feel free to skip to “How XGBoost works”.

Decision trees

Decision trees are arguably the most easily interpretable ML algorithms you can find and, if used in combination with the right techniques, can be quite powerful.

A decision tree has this name because of its visual shape, which looks like a tree, with a root and many nodes and leaves. Imagine you take a list of the Titanic’s survivors, with some information such as their age and gender, and a binary variable telling who survived the disaster and who didn’t. You now want to create a classification model, to predict who will survive, based on this data. A very simple one would look like this:

Image by author

As you can see, decision trees are just a sequence of simple decision rules that, combined, produce a prediction of the desired variable.

#data #python #statistics #machine-learning #data-science #data analytic

What is GEEK

Buddha Community

Which is a Good App Design Practice?

Which is a Good App Design Practice in 2020? Following amazing tips on mobile app design best practices and withdrawing the most typical mistakes will assist designers to create apps.
check out our blog on The Guide to Mobile App Design: Best Practices for 2020 and Beyond.

#which is a good app design practice? #mobile app ui design best practices #app design best practices

XGBoost — Frictionless Training on Datasets too Big for The Memory

TL;DR

XGBoost does best when it can train on the entire dataset at once. What to do when we don’t have enough RAM?

Bursting XGBoost training from your laptop to a Dask cluster allows training on out-of-core data, and saves hours of engineering work.

We demo an accessible workflow to run your training on a temporary, Python-native cluster straight from your own notebook or script. Check it out, run it on your own data!

What we’ll cover:

1. Loading and transforming data on a distributed cluster
2. Training XGBoost on Dask
3. How does the distributed XGBoost implementation work
4. Bursting to the cloud as and when needed
5. Visualizing performance of computations on a cluster with Dask Dashboards

Feel free to skip ahead to the part you’re most interested in!

Here is the code we will use, if you’d like to jump right in:

_View in GitHub, _or launch a hosted notebook in our JupyterLab environment.

Questions about the code? Join our Community Slack channel.

#dataset #xgboost #python #pandas-dataframe #dask #xgboost — frictionless training on datasets too big for the memory

Practice Problems: How To Join DataFrames in Pandas

Hey - Nick here! This page is a free excerpt from my $199 course Python for Finance, which is 50% off for the next 50 students.

If you want the full course, click here to sign up.

It’s now time for some practice problems! See below for details on how to proceed.

Course Repository & Practice Problems

All of the code for this course’s practice problems can be found in this GitHub repository.

There are two options that you can use to complete the practice problems:

• Open them in your browser with a platform called Binder using this link (recommended)
• Download the repository to your local computer and open them in a Jupyter Notebook using Anaconda (a bit more tedious)

Note that binder can take up to a minute to load the repository, so please be patient.

Within that repository, there is a folder called starter-files and a folder called finished-files. You should open the appropriate practice problems within the starter-files folder and only consult the corresponding file in the finished-files folder if you get stuck.

The repository is public, which means that you can suggest changes using a pull request later in this course if you’d like.

#dataframes #pandas #practice problems: how to join dataframes in pandas #how to join dataframes in pandas #practice #/pandas/issues.

Remote Teams and Good Time Management

This year’s global pandemic has forced the fast adjustment for many organizations from in-office normality to a remote working setup at whiplash speed. The rapid adoption of remote working is not easy though—even without the pressure of COVID.

Challenges are abundant for both those with remote working experience and those without. Managers and team leaders are struggling to keep their teams motivated and efficient. Remote teams are often seen as more difficult to manage, but the tips discussed in this article can help you overcome these challenges.

Empower Remote Team Members

Before digging deeper into time and task management, there are actually several basic steps that you need to complete in order to make remote teams effective. The first thing you want to do is making sure that team members can communicate easily and effectively, and that means establishing a way of communicating that everyone is comfortable with.

Most teams turn to Slack, but Slack is not always the best tool for the job. If your team puts emphasis on project management, for instance, using digital Kanban tools with built-in chat feature can be more effective and other communication tools such as Google Mail with Google Chat and Meet integrations are helpful. Microsoft has a similar suite of tools if you’re inclined to that choice of software.

To further empower team members, integrate a good task management platform. There is no way to keep track of everything when team members have to organize their tasks individually. The easier way to establish a baseline for remote working is by using a project or task management tool that turns tasks into blocks waiting to be managed such as Trello, Asana or Basecamp.

Lastly, encourage team members to create a productive environment that works for them. Some startups and corporations are starting to provide team members with aids to help them set up a more comfortable and functional home office. This is the kind of initiative that puts team members in the right mindset for effective remote working.

#best practices #blog #devops #best practices #devops best practices #remote working

XGBoost: theory and practice

Understand how one of the most popular algorithms works and how to use it

Introduction

XGBoost stands for eXtreme Gradient Boosting and it’s an open-source implementation of the gradient boosted trees algorithm. It has been one of the most popular machine learning techniques in Kaggle competitions, due to its prediction power and ease of use. It is a supervised learning algorithm that can be used for **regression **or **classification **tasks.

Regardless of its futuristic name, it’s actually not that hard to understand, as long as we first go through a few concepts: decision trees and gradient boosting. If you are already familiar with those, feel free to skip to “How XGBoost works”.

Decision trees

Decision trees are arguably the most easily interpretable ML algorithms you can find and, if used in combination with the right techniques, can be quite powerful.

A decision tree has this name because of its visual shape, which looks like a tree, with a root and many nodes and leaves. Imagine you take a list of the Titanic’s survivors, with some information such as their age and gender, and a binary variable telling who survived the disaster and who didn’t. You now want to create a classification model, to predict who will survive, based on this data. A very simple one would look like this:

Image by author

As you can see, decision trees are just a sequence of simple decision rules that, combined, produce a prediction of the desired variable.

#data #python #statistics #machine-learning #data-science #data analytic