Machine Learning Libraries : Scikit-Learn and Joblib

Machine Learning libraries are programming libraries that make it easier for a programmer to use machine learning algorithms. A programmer can write their own machine learning code, but this requires extensive knowledge and time. Libraries provide easy-to-use functions and classes that users can access.

• Not being able to scale up your data collection and model training to meet the requirements of your business, which is often the case when trying to implement traditional machine learning techniques.
• Having to manually curate data sets and train models that work well on small subsets of your original data set.

Some of the well-known libraries include Scikit-learn, joblib, NumPy, scipy, and many more 

In this article, we are going to discuss two well-known libraries, Scikit-learn and Joblib.

Scikit-Learn : Scikit-learn is the most popular and powerful library for machine learning in Python. It is a free and open-source machine-learning library providing many useful machine-learning algorithms. It provides a high-level scientific computing interface for data mining, statistical learning, data visualization, and much more. It supports both unsupervised and supervised learning using a large set of estimators, including the well-known logistic regression, decision trees, and random forest; dimensionality reduction techniques such as fastSparse and rawSparse, variable importance estimation; linear models, kernel functions (support vector machines), kernel methods; ensemble learning (random forests) with cross-validation; regression and classification on hierarchical structures; dimensionality reduction methods based on principal components analysis (PCA) or factor analysis (FA), as well as an implementation of Bayesian belief networks.

Scikit-learn makes it easy to learn and use machine learning in Python. It provides a flexible API for machine learning algorithms, data preparation, and model evaluations. The library has been designed for usability: all models are built on top of approximations that provide fast training and good generalization performance, which makes it easy to develop them.

The Most Important eatures of Scikit-Learn Are :

• It supports cross-platform installation, easy to install on Windows, Mac OS X, Linux, or any other operating system that supports Python.
• It supports Keras which is an open-source front-end framework for deep learning with a TensorFlow backend that runs on top of NumPy and SciPy.
• It supports all types of data types like text, images, time series, and others.
• Scikit-learn features several state-of-the-art algorithms that make it easy to develop scalable machine learning applications. For example, it includes support for multi-class classification as well as logistic regression with tree ensembles.
• The most significant feature of this library is its rich set of preprocessing functions that allow you to transform data before using the algorithms provided by Scikit-learn.

If you have installed Scikit-learn, go to the Python Shell and type:

>>> import sklearn

If you don’t have it installed, you can download the latest version from https://github.com/scikit-learn/scikit-learn/archive/latest.zip, unzip the file and then install it with Python:

>>> unzip scikit-learn_x.x.x.zip

>>> pip2 install scikit-learn_x.x.x

Joblib : Joblib is an open-source library that helps you quickly create and share ML models. It is a library for the Python programming language providing many different implementations of linear regression and classification algorithms, as well as support for many other machine learning tasks. It is used for managing and manipulating job descriptions. It contains modules for working with files in various formats, including the ability to parse and modify text files using Python syntax, which makes it useful for writing any kind of script that needs to manipulate data. The number one reason to use Joblib is that it lets you write your code in Python, but get the result back in C or C++. This means you can write your code once, and then reuse it on any platform that supports C++.

With Joblib, You Can :

• Create your machine learning model to train on your data.
• Use the built-in ML libraries to find and evaluate metrics for your model.
• Run tests on your model.

Joblib’s Main Features Include :

• Easy-to-use formatting tools that make it easy to create beautiful data visualizations.
• Flexible data parsing and transformation capabilities that allow you to easily extract or transform raw data into useful information for your projects
• A robust set of built-in functions that allow you to access common database systems from within Python
• Makes it easy to use multiple versions of Python on the same computer by providing a common interface that allows you to run multiple versions of Python at once, and it has tools that make it easy to switch between them.
• Support for many other languages, including Perl, Ruby, and Tcl/Tk. It’s a great library for managing your versatile application environment!
• Write your code with any language you want, even if you don’t know python.
• Run your code in the browser or on a server, just like you would with standard python.

You can use Joblib by importing the module and calling one of its functions:

import joblib

print(joblib.dump(1, “hex”))

print(joblib.load(“hex”))

Which is Better—Scikit-Learn or Joblib?

Scikit-learn and Joblib are two programming libraries designed to help with machine learning problems. They have different strengths and weaknesses, so which one you choose depends on what you’re trying to do.

Scikit-learn is based on sci-kit-learn, a popular Python library for machine learning, and has an extensive set of Python functions for performing statistical analyses. It has a large collection of common algorithms, like linear regression and classification tasks, as well as some more advanced ones such as support vector machines (SVMs). This can make it easier to fit your problem into a common framework.

Joblib is similar in that it’s also based on sci-kit-learn, but it was developed by Google’s DeepMind AI research group. It focuses on neural networks and deep learning algorithms, which are becoming popular because they’re capable of making complex decisions without needing any human input.