Introduction

New algorithms are difficult to come by and 2022 is likely going to be no exception. However, there are still a few machine learning algorithms and python libraries that will be more popular moving forward.

The reason these stand out from the rest is that they include several benefits that are not as prevalent in other algorithms, which I will discuss in more detail.

Whether it is the benefit of being able to use different data types in your models, or incorporating built-in algorithms into your current company infrastructure, or even comparing success metrics of several algorithms in one spot, you can expect these to all become more popular in the next year for a variety of reasons.

Let’s dive a little deeper into some of these emerging algorithms and libraries for 2022 below.

CatBoost

Photo by Michael Sum on Unsplash [2].

Perhaps the newest, with updates frequently as it becomes more popular, is CatBoost. This machine-learning algorithm is especially useful for data scientists who are dealing with data that is categorical. You can think of the benefits of Random Forest and XGBoost algorithms, and apply most of them to CatBoost, while also reaping even more benefits.

Here are the main benefits of CatBoost:

  • Do not need to worry about parameters tuning — default usually wins, and it might not be worth it to manually adjust unless you are aiming for specific distributions of error by manually changing values
  • More accurate — less overfitting, and tend to have more accurate results when you are using more categorical features
  • Fast  this algorithm tends to be quicker than other tree-based algorithms because it does not have to worry about large, sparse datasets with one-hot encoding applied for example, because it uses a type of target encoding instead
  • Predict quicker — just how you can train faster, you can also predict using your CatBoost model quicker
  • SHAP — the library is integrated for easy explainability for feature importance on the overall model, as well as specific predictions

Overall, CatBoost is great because it is easy to use, powerful, and competitive in the algorithm space as well as something to include on your resume. It can help you create better models to ultimately make your projects for your company better.

CatBoost documentation here [3].

DeepAR Forecasting

Photo by NOAA on Unsplash [4].

This algorithm is built into the popular platform, Amazon SageMaker, which could be great news if your company currently is on the AWS stack or is willing to use it. It is utilized for supervised learning in forecasting/time-series applications with the help of recurrent neural networks.

Here are some examples of the input file fields to expect for using this algorithm:

  • start
  • target
  • dynamic _feat
  • cat

Here are some of the benefits of using this algorithm/architecture:

  • Easy modeling — build/train/deploy in the same place, rather quickly
  • Easy architecture — focus on less coding are more on your data and the business questions that you need to solve

There is of course more to this algorithm so I am limiting the amount of information since not everyone reading is using AWS.

DeepAR Forecasting Algorithm documentation here [5].

PyCaret

Photo by Jonathan Pielmayer on Unsplash [6].

Because there are not so many new algorithms to discuss, I wanted to include one library that is able to compare several algorithms, some of which might be updated, and therefore new. This Python library is referenced as open-source and low-code. It has allowed me to be more aware of new and upcoming machine learning algorithms when I get to the point of comparing and ultimately picking the final algorithm for my data science model.

Here are some of the benefits of using this library full of algorithms:

  • Less time coding — you do not need to import libraries and set up each and every preprocess step that is unique to each algorithm, and instead, can fill in a few parameters allowing you to compare pretty much every algorithm you have ever heard of side by side
  • Easy to use — as libraries evolve, so does their ease of use.
  • End-to-end processing — can research your data science problem from transforming data to predicting results
  • Integrates well — can utilize AutoML in Power BI
  • Blend and stack — can join different algorithms to reap more benefits
  • Calibrate and optimize model
  • Association rule mining
  • and most importantly, compares 20+ algorithms at a time

Overall, this library is not directly a new algorithm, but it will most likely include an algorithm that will be new in 2022, or at least the most recent, even ones like CatBoost mentioned above are included in this library — and that is how I found out about it. With that being said, I think it is important to include this library so you can keep up-to-date with not only 2022, but also algorithms that might be older that you might have not heard of before or have missed, as you compare them side by side with their easy user interface.

PyCaret documentation here [7].

Summary

If you think this list is short, then you will realize that not every year has a new group of machine learning algorithms. I hope these three mentioned here will increase their documentation (or peer documentation) and popularity because they are so great, and are different from the usual logistic regression/decision trees, etc.

To summarize, here are some of the new machine learning algorithms to look forward to in 2022:

* CatBoost - algorithm* DeepAR Forecasting - algorithm/package* PyCaret - library including new algorithms

I hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with these included. Why or why not? What other algorithms or packages/libraries do you think we could include that are just or more important? These can certainly be clarified even further, but I hope I was able to shed some light on some more unique and machine learning algorithms and libraries. 

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