May 21, 2022

Top Machine Learning Algorithms and Python Libraries for 2022


New algorithms are hard to come by and 2022 is probably no exception. However, there are still some machine learning algorithms and Python libraries that will become more popular as time goes on.

The reason they differ from the rest is that they include some advantages that are not as common as other algorithms, which I will discuss in more detail.

Whether it’s being able to use different data types in your models or incorporating built-in algorithms into your existing company’s infrastructure, or comparing the success metrics of multiple algorithms in one place, you can expect them to be more popular. The following year for various reasons.

Let’s dive deeper into these emerging algorithms and libraries for 2022 below.



Probably the newest, as it becomes more popular with frequent updates, is a boost. This machine-learning algorithm is especially useful for data scientists who are working with specific data. You can think of the benefits of random forest and XGBoost algorithms, and make the most of them as you reap the benefits.

No need to worry about parameter tuning – defaults are usually won, and manually adjusting may not be worthwhile if you don’t aim for specific distribution of errors by manually changing the values.
More accurate – less overfitting, and you tend to get more accurate results when you use more explicit features
Fast – This algorithm tends to be faster than other tree-based algorithms because it doesn’t have to worry about large, scattered datasets applying one-hot encoding because it uses one type of target encoding instead.
Quickly predict – how fast you can train, you can also predict using your CatBoost model quickly
SHAP – The library integrates the importance of the features of the overall model, as well as the easy interpretation of specific predictions.

Overall, CatBoost is great because it’s easy to use, powerful and competitive in the algorithm space, as well as something to incorporate into your resume. It can help you build better models for your company in the end to make your projects better.

DeepAR Forecasting

DeepAR Forecasting

This algorithm is built on the popular platform, Amazon SageMaker, which can be great news if your company is currently on the AWS stack or willing to use it. It is used for supervised learning in predictive / time-series applications using recurrent neural networks.

The beginning
The goal
Dynamic _fit
Easy Modeling – Build / Train / Deploy in one place, rather quickly
Simple Architecture – Focus less on coding and more on the data and business questions you need to solve


There is definitely more to this algorithm so I’m limiting the amount of information because not everyone is using AWS.

DeepAR Forecasting Algorithm documentation here [5].




Since there aren’t so many new algorithms to discuss, I wanted to include a library capable of comparing several algorithms, some of which could be updated and so new. This Python library is referred to as open-source and low-code. This has allowed me to be more aware of new and upcoming machine learning algorithms when I can compare and finally pick the final algorithm for my data science model.


Less coding time – you don’t have to import libraries and set up each preprocess step unique to each algorithm, and instead, fill in a few parameters that you can compare to each algorithm you’ve heard. Side
Easy to use – Libraries are easy to use as they evolve.
End-to-end processing – can research your data science problem from data conversion to result in prediction
Integrates well – can use AutoML in Power BI
Blend and Stack – You can combine different algorithms to get more benefits
Calibrate and optimize the model
The rules of the association are mining
And most importantly, compare 20+ algorithms at a time

Overall, this library isn’t directly a new algorithm, but it will probably include an algorithm that will be new in 2022, or at least the most recent, even included in this library like the CatBoost mentioned above – and that’s how I learned about it. That being said, I think it’s important to include this library so that you can not only keep up-to-date with 2022 but also algorithms that you may not have heard or missed before, such as older than you compare. Along with their simple user interface.


If you think this list is short, you will realize that there is a new group of machine learning algorithms every year. I hope the three mentioned here will increase their documentation (or peer documentation) and popularity because they are great and different from the general logistic regression/decision tree etc.

I hope you found my article both interesting and useful. If you agree or disagree with this inclusion please feel free to comment below. Why or why not? What other algorithms or packages/libraries do you think we can include that are simple or more important? These can certainly be made more clear, but I hope I have been able to shed some more light on some of the more unique and machine learning algorithms and libraries.

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