Whether you’re new to the world of Python or not, then we’re sure that you might agree with us when we say that scikit-learn is definitely one of the most popular packages used on the software, especially where Machine Learning is concerned.
However, despite how commonly-used scikit-learn is, there are many beginners to the software that often find themselves getting confused about what they should name the package.
Why? Well, this is due to the way that it looks as though it features two names: scikit-learn and sklearn.
This can cause plenty of confusion. However, this is where we come in to lend you a helping hand.
In this guide, we are going to be discussing whether there is any difference between these two packages, as well as provide you with some helpful guidance on which one you should install and use as your source code.
Just keep on reading to discover all you need to know about the difference between scikit-learn and sklearn in Python!
What Is Scikit-Learn?
To kick off this guide, we are first going to be taking a moment to talk you through what the main package used on Python is: Scikit-Learn!
To cut a long story short, the project Scikit-learn was initially developed as a part of Google Summer of Code in 2007, although it wasn’t officially released to the public until 2010.
Scikit-learn is a type of open source Machine Learning that was developed specifically for use as a Python package.
Unlike some of the other packages on the Python platform, Scikit-learn is able to offer plenty of functionality and ease of use, all while being able to support both supervised and unsupervised learning.
In addition to this, Scikit-learn is also designed to be able to provide useful tools for model development, selection and evaluation as well as a vast array of other awesome functionality features that help to make data-processing a breeze.
To be more specific, Scikit-learn’s top functionality includes the ability to offer clustering, regression classification, dimensionality reduction, model selection and even the convenience of pre-processing.
In other words, regardless of whether you’re an experienced coder or newbie to the Python platform, Scikit-learn can be used by anyone, regardless of skill level or coding ability, in order to improve Machine Learning capabilities, ease-of-use as well as functionality.
Are There Any Differences Between Scikit-Learn And Sklearn In Python?
So, now that we have covered what Scikit-learn is, we’re sure that you’ll be keen to learn whether or not there are any differences with regards to Python’s other popular package: Sklearn.
The answer to this question is, no, there aren’t any main differences between these two types of packages within the Python software, because they actually refer to the same package!
However, despite this, there are still a few main things to keep in mind that you should be aware of before picking a package to use.
By keeping these in mind while using Python, you will be able to install your package in the most efficient and optimized way possible.
Should You Install The Machine Learning Package With Scikit-Learn Or Sklearn Identifiers?
The first main point you should be aware of is that you will be able to install the package by simply opting to use either the code command of scikit-learn or sklearn.
No matter which identifier you use, both will allow you to install the same package.
However, while you might be able to install the same package using either identifier, it’s important to note that it is recommended that you opt to install this Machine Learning package through scikit-learn via pip to install the bulk of the package.
Along with this, you should also ensure that you are using the sklearn identifier as the source code.
If you do not install the package in this way, then you’re likely going to find yourself dealing with a few problems along the way.
For example, if you were to install the package using solely the sklearn identifier through pip, then you are more than likely going to discover within your coding a frustrating sklearn 0.0 entry.
Along with this, if you don’t install the package by using the method that we have shared with you above, there is also a high chance that you will be unable to uninstall the package from your Python software’s coding.
So, in other words, if you were to try and uninstall the package by uninstalling sklearn, you are going to find that the package itself hasn’t actually been installed.
Wondering why? Well, the main reason for this is that Sklearn is merely a dummy project created by PyPi.
With that being said, when a Python user decides to install the package using the Sklearn identifier, the dummy project will simply be redirected to install the package associated with the identifier Scikit-learn.
This means that, if you decided that you wanted to uninstall the package associated with the Sklearn identifier, all you will be doing is simply uninstalling the dummy package, but not the actual package itself.
So, the package will remain installed because it is associated with the identifier Scikit-learn.
Which Identifier Should You Use To Import The Package?
In order to make sure that you correctly install the package, you are also going to need to make sure that you are using the correct identifier to import the package.
Even though the identifier sklearn is associated with the dummy package, it is highly recommended that you opt to use this identifier in order to successfully import that package while you are coding.
It is very important that you do not use the Scikit-learn identifier to import your package because you will ultimately end up with an error in your coding. In order to avoid this, you should make sure that you are only using the sklearn identifier to import
There we have it! You’ve reached the end of the guide.
After taking the time to read through everything that we have talked you through above, we hope that we have been able to shed some light on the confusion that often surrounds both Scikit-learn and Sklearn while building Machine Learning functionality while using the Python Platform.
Even though it is certainly possible to install the same package by using either one of these identifiers, remember that it is highly recommended that you install your ML package via the scikit-learn code identifier, while also ensuring that you are making your source code with the sklearn identifier.
By doing it this way, you will avoid any of the frustrating roadblocks that we have mentioned above.
As a side note, before you go, why don’t you consider giving this page a bookmark?
That way, if you ever needed to come back and refresh your knowledge on these two identifiers, you’ll know exactly where to find this page.
Thanks for taking the time to read through this guide, and we hope that you have fun developing your Machine Learning capabilities within Python!
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