Machine learning is one of the most future-oriented fields today. And it’s constantly evolving. People encounter it everywhere ― from digital assistants like Siri and Alexa to technology that trains smart home devices, such as thermostats that automatically adjust the temperature based on climate and usage.
LightGBM is a tool used by programmers, analysts, financial sector employees, and numerous other tech professionals. LightGBM can be used with different programming languages, so you can work with the code you are already familiar with, rather than learning a new language from scratch. LightGBM is also fast, which makes it suitable for Big Data applications. In this article, we explain what LightGBM is and how it can benefit you.
Before we start, let’s talk about machine learning
Before we get into LightGBM itself, it is crucial to understand what Machine learning is, of which our tool is a part.
Machine learning is a specialized way of teaching computers without the need for programming ― somewhat similar to a child, learning to classify objects and events independently and to determine the relationships between them.
There are several types of machine learning: supervised, unsupervised, and reinforcement learning. LightGBM is used in the first type, for which it is also important to have labels — essentially, that we care about, which help us identify an object.
For example, when classifying pictures of cats, in order to make a picture searchable, we add a label to it e.g. "This is a picture of a cat". Aside from that, there are three equally important components of machine learning.
- Data. Collected in different ways. The more data, the more effective machine learning is, and the more accurate the future results.
- Features. Determines what parameters the machine learning is based on.
- Algorithms. The choice of machine learning method, assuming good data is available, will affect the accuracy, speed, and size of the finished model.
Machine learning is already implemented in nearly every area of human activity. According to Stanford University, the number of ML-enabled startups increased 14 times between 2000 and 2018. Today, ML is used in areas such as robotics, marketing, security, the financial sector, public catering, medicine, and so on.
Definition of LightGBM
LightGBM is a framework (a ready-made set of tools, libraries, and guidelines for quick development), based on which you can write your code. It’s a free and open-source tool developed by Microsoft.
LightGBM is suitable for different operating systems, including Windows, Linux, and MacOS.The framework is also compatible with three programming languages ― Python, C++, R, and C#, which allows more programmers to use it.
Imagine, you need to select pictures of white, black, and red cats on the web. First, the program finds pictures of animals, then narrows it down to furry animals, then, finally, to cats. After that, the program categorizes them by white, black, and red. This is one of the uses of LightGBM.
The two main tasks that LightGBM solves
Classification. This analyzes the data offered and assigns each object to a particular group. Imagine you have a lot of clothes in your wardrobe and you need to sort them into several categories. LightGBM then divides all the clothes into trousers, dresses, T-shirts, and underwear.
Regression. Predicts a specific numerical value of an independent variable. This is how you would predict the weather, for instance. You look at all the data that you have collected over the years and make conclusions about what the weather might be like tomorrow.
Advantages of LightGBM
Fast learning speed and high efficiency. LightGBM uses a histogram-based algorithm, which means it presents the data in a more visual way, which speeds up the learning process. Furthermore, GOSS speeds up the process considerably, even compared to XGBoost, which works with already pre-processed data.
Lower memory usage. In the previous paragraph about EFB, we said that LightGBM can combine mutually exclusive functions. The framework also takes advantage of the vertical growth of the decision tree. All this reduces memory usage. This feature is unique to LightGBM.
Better accuracy than any other amplification algorithm. LightBGM generates much more complex trees than XGBoost ― for example, by following a leaf-by-leaf rather than a level-by-level approach, which is a major factor in achieving higher accuracy.
Compatibility with large datasets. LightGBM can work equally well with large datasets, while significantly reducing training time, compared to, for example, XGBoost, which is also capable of analyzing large amounts of data but does so at a much slower pace.
So, why learn LightGBM?
LightGBM is a handy Machine Learning framework for working with big data and is just one of many available ML tools. You need knowledge of basic programming languages in order to start using them. For a full-fledged career in machine learning, you will also require additional tools. Meanwhile, LightGBM is one of the cutting-edge technologies in the field today. You can master it, along with other tools, by enrolling in the Practicum Data Science Bootcamp.
According to Indeed.com, the average annual salary for a machine learning specialist in the US is $147,552. Newcomers normally start at $90,000, while experienced professionals can earn up to $240,000. The highest-paying states for ML specialists are New York, Cupertino, San Francisco, and San Jose. Meanwhile, data scientists' salaries are slightly lower, ranging from $76,000 for newcomers to $203,000 for experienced professionals, and the highest-paying locations are Palo Alto, San Francisco, and New York.
Therefore, learning LightGBM will not only help you dive into the promising world of machine learning but will also assist in gaining a well-paid and in-demand profession that will only continue to grow in the future!