When it comes to a software program that you can use for free, many options are available on the market, especially when it comes to open-source software.
For a newcomer to coding, it can all feel a little overwhelming at first, especially when you’re going in blind.
However, as you slowly get to grips with many of the key terms and the systems and languages (of programming) that are used, you’ll slowly gravitate to one or two systems, with systems like Python being some of the most popular out there.
When it comes to finding data analysis tools for python, and thinking about the best open-source libraries that many people rely on, you’re likely to find that two names keep popping up: Scikit-Learn, and Scipy.
Both are phenomenal tools to have plenty of applications when you have the skills to use them in Python. But what exactly are their uses, even? And what makes them different from each other?
In this guide, we’ll break down all this information and more. By the end of it, you should have a much better understanding of what these two machine-learning libraries are used for.
This will be a great tool if you’re someone who is planning on learning open-source programming and need to know where to look for your data and inputs.
What Is Scikit-Learn?
To start this guide, we’re going to be taking a look at Scikit-Learn, and what makes it such a good open-source library to use in Python.
Scikit-learn started in 2007 as a Google Summer of Code project, though it quickly became its own vast library of sources as it went through various states of renewal and collection data analyst tools.
The first version of the open-source library would go public in 2010
What Is It Used For?
Scikit-Learn is probably one of the best open-source libraries to use when concerning aspects of machine learning.
Using high-performance linear algebra and array operations that the library is known for, Scikit-Learn is phenomenal to produce algorithmic decisions of virtually all kinds, from calculations for categorizing and identifying sets of data, automatically grouping datasets introduced into a system, and determining and predicting values that are likely to appear in the future, based on established data and routines.
The algorithms used in Scikit-Learn’s system focus primarily on the representation of data sets, the optimization of data sets into wider groups, then the optimization of those sets, to better help predictions made in the future.
As all these factors imply, this is a pretty powerful piece of machine learning essentials here!
This makes Scikit-Learn one of the easiest Machine Learning research tools for coders and programmers.
What Is SciPy?
So, now that we have outlined what exactly Scikit-Learn is, we can turn to the other main open-source library in this guide.
Being first developed in 2001, Scipy has been used in scientific computing, technical computing, and search optimization for over 20 years at this point.
The name itself stands for ‘Scientific Python’, and was built on NumPy, another Python library of matrices.
What Is It Used For?
Scipy was developed to do many of the same tasks and routines that NumPy was designed to do.
Only in Scipy, they are better optimized, and extra features were added to make it better at handling data sciences, hence why it is so popular with technical computing science, as well as solving mathematical and other scientific problems.
So, Scipy kind of does what NumPy does, only faster, and better!
Comparisons – What’s So Different
So, now that we’ve covered these two libraries in Python, we can now start to actually compare them with one another.
For starters, simply looking at these two libraries stated aims, there are a lot of differences between the two.
While SciPy is mainly used for better streamlining and speeding up the programming and system analysis of preexisting libraries like NumPy, Scikit-Learn is explicitly focused on improving machine learning capabilities in the systems in that it is used.
Ease Of Use
One of the other factors that look pretty clear once you look closer at these systems is how easy they are to use.
SciPy is a functions’ kit that, built in 2001, isn’t exactly the easiest-to-use library of computing systems, especially when it comes to newcomers unfamiliar with it.
Scikit-Learn, meanwhile, marks itself apart from SciPy by benign a coding library that is relatively easy to use once the programs have been set up to analyze data.
Again, this is relative. We’re not confident at how easily a random person off the street could handle it.
But as far as machine learning systems go, Scikit-Learn is regarded as one of the simplest.
Comparisons – How Are They Similar?
We’ve talked at length about the differences between these two systems. Are there any similarities between these two libraries for Python? Outside being used in Python, of course.
Well, as it turns out, there are quite a few commonalities between these two. Certainly, more than there might look like at first.
For example, both operating libraries use NumPy as the basis of their functions, so they share a common ancestry, to borrow a biological term.
In fact, in some regards, many of Scikit’s systems are built on top of SciPy’s own, so it isn’t its distinct library of codes, but one that uses the data formatting functions to its own needs.
Frequently Asked Questions
Are There Any Alternatives To Scikit-learn?
While probably the most famous and simple, Scikit is by no means the only machine learning library that is open-source out there.
MLlib is Spark’s own machine learning library that is also noted as being one of the easier systems to utilize, while Weka uses machine learning for data mining purposes, and has many of the same features that Scikit does, from optimization, to even value predictions.
And, of course, Google has its machine learning library, in the form of Google Cloud TPU, which is touted as being excellent at accelerating machine learning processes.
Is Scikit-Learn Better Than Tensorflow?
That depends on what your needs are in the first place.
Generally speaking, TensorFlow is considered much better at deep learning and neural network function, while Scikit-learn is a general machine learning pack.
And TensorFlow is indeed likely the better well-known example of a machine learning program, at least when compared to Scikit-learn.
Try and establish what your stated goals are with your machine learning program before continuing.
So, while it is clear that these two systems are very different from each other, having completely different aims, they really on one another to function at their best.
That is certainly the case for Scikit-Learn, whose own calculations and optimizations wouldn’t be possible without those that SciPy employs.
If we wanted to use the evolutionary metaphor one more time from before, you can think of Scikit-Learn, SciPy, and NumPy as different steps in an evolutionary family.
First came NumPy, with its array of mathematical functions.
Then came SciPy, with a further streamlining of that process.
Then you have the latest system, Scikit-Learn, which uses everything that has come before it (and more), to create a library of effective, easy-to-learn programming and coding systems for artificial intelligence development.
That’s quite the family history, isn’t it?