Scikit-learn or TensorFlow: What’s the Difference?

Key takeaways

When deciding between scikit-learn and TensorFlow, the scale of your machine learning project is a crucial factor.

• Scikit-learn is a great option for smaller machine learning tasks, while TensorFlow stands out for its deep learning capabilities. 

• Knowledge of machine learning libraries and frameworks like scikit-learn and TensorFlow is important for artificial intelligence careers.

• You can use scikit-learn alongside other machine learning libraries, such as Matplotlib and NumPy.

Discover whether scikit-learn or TensorFlow is right for your machine learning project. If you’re ready to gain in-demand deep learning skills, consider earning an IBM Deep Learning with PyTorch, Keras, and TensorFlow Professional Certificate, where you will have the opportunity to develop job-ready skills that employers are looking for, such as reinforcement learning, transfer learning, and image analysis, using various machine learning and deep learning libraries.

Why learn scikit-learn and TensorFlow?

Career opportunities in artificial intelligence (AI) and machine learning are rapidly growing, creating significant demand for employees with specialized skills in this transformative space. Gaining proficiency with machine learning frameworks and libraries is critical if you want to work in the field. Two of those tools to consider learning include scikit-learn and TensorFlow, both of which the machine learning field uses widely to streamline processes and efficiently develop machine learning models. As a plus, scikit-learn and TensorFlow can also help make it easier for beginners to create machine learning models.

What is scikit-learn?

Scikit-learn is a machine learning library written in Python and built with other libraries, including NumPy, SciPy, and Matplotlib, to help facilitate numerical and scientific computing, giving scikit-learn the ability to perform complex calculations and manage data sets. Scikit-learn gives you access to estimators, pre-built machine learning algorithms that you can fit to your data. One of the reasons behind scikit-learn's popularity is its application programming interface (API), which makes it possible for you to simplify the model training process and implement various types of algorithms such as k-means clustering, random forests, gradient boosting, and support vector machines.

What is scikit-learn used for?

Scikit-learn provides access to tools that will support you in developing machine learning models. Scikit-learn covers various areas in this process, including supervised and unsupervised learning, data preprocessing, and model fitting, evaluation, and selection. 

Scikit-learn is also a valuable library for data science tasks. For example, you can integrate scikit-learn with other Python libraries like Matplotlib and pandas to develop visualizations and analyze data. So, whether you’re building a machine learning model or looking to derive valuable insights from data, scikit-learn can help.

How does scikit-learn work?

To work with scikit-learn, you first need to install it. You can install the latest version following scikit-learn's installation guide, or simply install it directly from your operating system or Python distribution if available. Once installed, you can access pretrained neural networks and algorithms, process data sets, and perform other machine learning tasks, without requiring extensive knowledge of more complex areas like linear algebra or calculus, which would otherwise make machine learning more challenging.

Pros and cons of scikit-learn

The advantage of scikit-learn is that it makes machine learning accessible without having to write as much code, in addition to providing you with algorithms ready for use. This ultimately simplifies and helps you to avoid errors during what can typically be a challenging process. Scikit-learn's API also allows you to work back and forth with different algorithms without needing to learn a new interface or syntax, making it an exceptionally user-friendly tool.

Scikit-learn does fall short in some areas, such as inefficiency when dealing with big data, as it’s better suited for small and medium-sized data sets. Compared to other libraries like TensorFlow, it doesn’t perform as well in deep learning. 

What is TensorFlow?

TensorFlow, developed by Google, is another widely used machine learning library with tools for training and developing machine learning models. TensorFlow is accessible on a range of different JavaScript platforms, whether that be internet browsers, mobile devices, the cloud, or servers, so that you can use it basically anywhere. TensorFlow utilizes user-friendly APIs like Keras to make building and debugging models easier. As an end-to-end platform, TensorFlow provides you with the support you need during the entire process of building machine learning models, from the concept stage all the way through to deploying your model.

Is TensorFlow better than scikit-learn?

TensorFlow and scikit-learn serve different purposes for machine learning and deep learning, so the better library is simply the one most suitable for the specific task you’re working on. For example, scikit-learn is designed for more basic machine learning algorithms like regression, clustering, and classification, while TensorFlow excels in deep learning neural network architecture. However, scikit-learn is capable of working with a wider range of data types.

What is TensorFlow used for?

You will likely primarily use TensorFlow to help you process data for training, build a machine learning model, and train it. You can build and train machine learning models using TensorFlow to perform several different tasks, like object recognition, image creation using generative AI, or natural language processing to give machines the ability to understand text. Real-world use cases of TensorFlow include training models to accurately assess medical imaging and teaching self-driving cars to identify potentially hazardous obstacles on the road.

How does TensorFlow work?

To work with TensorFlow, you must first install it on either Windows 7 or later, Python 3.9-3.12, macOS 12.0 or later, Ubuntu 16.04 or later, or WSL2. After installing TensorFlow, you are ready to create, train, and deploy machine learning models, usually using arrays referred to as tensors, where a computational graph describes how data flows while training the model. Ultimately, TensorFlow works to simplify the process of developing machine learning models by providing you with a wide range of tools to help you along the different stages.

Pros and cons of TensorFlow

TensorFlow stands out for its scalability and deep learning capabilities. Thanks to the Keras API, it also makes machine learning achievable for beginners and is accessible regardless of the platform or language you use. Another advantage of TensorFlow is the simplicity of debugging and prototyping through eager execution.

While TensorFlow offers a thorough list of tools to support the entire machine learning development process, other machine learning libraries may outperform TensorFlow in specific areas. For example, PyTorch is another machine learning library that would be better suited for prototyping, or scikit-learn could outperform TensorFlow in smaller-scale projects.

Read more: TensorFlow or PyTorch: What’s the Difference?

Scikit-learn or TensorFlow: Which should you use?

When deciding between using scikit-learn or TensorFlow, the most crucial factor to consider is the size of your project. While scikit-learn excels in smaller machine learning tasks, TensorFlow outperforms its counterpart on larger scales, such as deep learning. Additionally, if you do much data science work in Python, scikit-learn's direct relationship with SciPy, NumPy, and Matplotlib is valuable. 

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