Have you ever been confused about whether you should use Scikit-learn or TensorFlow for your data science or machine learning project? Have you wondered about the pros and cons and, most importantly, the primary differences between the two? Well, no more. In this article, we will discuss both these toolkits in detail.

What Is Scikit-Learn?

Scikit-learn is an open-sourced Python library and includes a variety of unsupervised and supervised learning techniques. It is based on technologies and libraries like Matplotlib, Pandas and NumPy and helps simplify the coding task.

Scikit-learn features include:

  • Model selection
  • Classification (K-Nearest Neighbors inclusive)
  • Preprocessing (Min-Max Normalization inclusive)
  • Clustering (K-Means++ and K-Means inclusive)
  • Regression (Logistic and Linear Regression inclusive)

To understand Scikit-learn better, let us discuss some of the uses, pros and cons of Scikit-learn.

Use of Scikit-Learn

Scikit-learn allows us to define machine learning algorithms and compare them to one another, as well as offers tools to preprocess data. K-means clustering, Random Forests, Support Vector Machines, and any other machine learning model that we might want to develop are all included in Scikit-learn.

Scikit-learn’s true strength resides in its model assessment and selection architecture, which allows us to cross-validate and perform multiple hyperparameter searches on our models. Scikit-learn also helps us choose the best model for our work.

Let us now look at some pros and cons of using Scikit-learn.


  • Users who want to connect the algorithms to their platforms will find detailed API documentation on the scikit-learn website.
  • Many contributors, authors, and a large international online community support and update Scikit-learn.
  • It's simple to use.
  • The library is released under the BSD license, making it available for free with only the most basic licensing and legal constraints.
  • The scikit-learn package is extremely adaptable and useful, and it can be used for a variety of real-world tasks such as developing neuroimages, predicting consumer behavior, and so on.


  • Not a great choice if one prefers in-depth learning.
  • Provides a simple abstraction that may tempt junior data scientists to proceed without first learning the basics.

What Is TensorFlow?

TensorFlow is a Google-maintained open-source framework for prototyping and assessing machine learning models, primarily neural networks. TensorFlow is written in a variety of languages, including Swift, Python, Go, Javascript, Java, and C++, and includes community-built support for a variety of others. 

TensorFlow organizes low-level numerical programming in a high-level and abstract manner. It also supports libraries that allow our applications to run on a standard CPU without modification. Linux, Android, macOS, and Windows are among TensorFlow’s supported systems. The Google Cloud Machine Learning Engine can also run TensorFlow models without the use of a traditional computing platform.

Now that we understand TensorFlow a little better, let us now dive into some of its uses, and the pros and cons of using TensorFlow.

Use of TensorFlow 

Although TensorFlow is generally linked with neural networks, it is well-tuned for any of the machine learning methods that employ gradients in general (such as Boosted Trees). TensorFlow also offers TensorBoard, a visualization tool for comparing and tracking our learned models.

TensorFlow's attractiveness stems from its speed and neural network optimization. Very few frameworks can match TensorFlow's ability to run models on GPUs, CPUs, GPUs, and TPUs.

Let us look at some pros and cons of using TensorFlow.


  • It can quickly and easily calculate mathematical expressions.
  • TensorFlow can generate numerous sequence models and train a deep neural network for handwritten digit classification.
  • TensorFlow offers a unique feature that allows it to improve memory and data usage at the same time.
  • It has Google's support. It provides regular new feature releases, quick upgrades, and smooth performance.
  • TensorFlow is built to work with a variety of backend software (ASICs, GPUs, and so on) and to be extremely parallel.
  • TensorFlow has a strong community behind it.
  • It allows us to execute subparts of a graph, giving it an advantage because discrete data may be introduced and retrieved.
  • TensorFlow's computation graph visualizations are superior when compared to intrinsic libraries like Theano and Torch.
  • It uses a novel approach that allows us to track many metrics as well as monitor our models' training progress.
  • Its performance is excellent, and it is on par with the best in the industry.
  • The libraries are installed on a hardware machine, a complicated cellular device connected to the computer that enables scalability.


  • When compared to its competitors, TensorFlow falls short on both usability and speed.
  • Currently, NVIDIA is the only GPU supported, and Python is the only full language supported, which is a disadvantage since there are a growing number of other deep learning languages.
  • When it comes to variable-length sequences, the characteristic is much more important. Unfortunately, TensorFlow lacks capabilities; however, finite folding is the best approach to solve this.
  • There are many users that prefer to work in a Windows environment rather than on Linux, but TensorFlow does not meet their needs. However, if we are a Windows user, we may alternatively install it via the Python Package Library (pip) or conda.
  • It is an entry-level game with a high learning curve.
  • OpenCL is not supported.
  • Because of TensorFlow’s unique structure, it is tough to discover and troubleshoot errors.
  • TensorFlow lags in the area of computational speed because we focus on the production environment rather than performance.
  • There is no requirement for ultra-low-level system requisites.
  • It has a prerequisite of a solid foundation in advanced mathematics and linear algebra, as well as a thorough understanding of machine learning, making it beginner unfriendly.

Since we have discussed Scikit-learn and TensorFlow separately along with their uses, pros and cons, let us now learn more about Scikit-learn vs TensorFlow comparison.

Scikit-Learn vs TensorFlow Comparison

Below is the comparison table of Scikit-learn vs TensorFlow.



A neural network is used to optimize TensorFlow.

With other frameworks like XGBoost, Scikit-learn is more flexible.

TensorFlow is utilized in the design process to assist developers, as well as for benchmarking new models.

Scikit-learn is also used to create and benchmark the new model, as well as to design and assist developers.

TensorFlow is a low-level library that helps in implementing machine learning techniques and algorithms.

The machine learning algorithm is also implemented using Scikit-learn, a higher-level library.

It is a third-party module. However, it is more widely used.

This is also a third-party module, Scikit-learn, which is less popular than TensorFlow.

TensorFlow uses the base class to implement all of its algorithms.

All Scikit-learn algorithms are used as a base estimator.

TensorFlow is a deep learning framework.

Scikit-learn is mostly used in machine learning applications.

The neural network is used indirectly by TensorFlow.

In practice, Scikit-learn is utilized with a wide range of models.

It provides under-the-hood specialization optimization, making it easier to compare neural network models and TensorFlow models.

It is possible to compare completely distinct variants of machine learning models using Scikit-learn.

TensorFlow is a barebones neural network implementation.

A neural network model that is barebone is not implemented in Scikit-learn.

Master Scikit-Learn and TensorFlow With Simplilearn

Scikit-learn and TensorFlow were designed to assist developers in creating and benchmarking new models, so their functional implementations are very similar, with the exception that Scikit-learn is used in practice with a broader range of models, whereas TensorFlow's implied use is for neural networks.

To learn more about these tools and level up as a senior developer in the field of data science, check out Simplilearn’s Data Science Certification. This program features exclusive hackathons, masterclasses by real-life experts, and Ask Me Anything sessions by IBM, and so much more. Explore and enroll now!