TensorFlow vs. Scikit-Learn: How Do They Compare?

In this article, we’ll compare TensorFlow and Scikit-Learn side-by-side to see what they do and how you can use them.

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After reading an exciting paper or cleaning your data, what’s the next step? You want to start building your machine learning models and testing them—after all, that’s the exciting part of machine learning.

From prototyping new models to evaluating and ultimately deploying the best model(s), you need a consistent framework to keep track of your results and make different models comparable. This is where TensorFlow and Scikit-Learn can help you.

In this article, we’ll compare TensorFlow and Scikit-Learn side-by-side to see what they do and how you can use them.

*Looking for the Colab Notebook for this post? Find it right here.*

What is Scikit-Learn used for?

Scikit-Learn is an open-source package for creating and evaluating machine learning models of all flavors in Python.

Scikit-Learn allows you to define machine learning algorithms and evaluate many different algorithms against one another; it also includes tools to help you preprocess your dataset. The Scikit-Learn includes a diverse cast of machine learning models including Support Vector Machines, Random Forests, K-means clustering, and any model you want to implement yourself.

The real power of Scikit-Learn lies in its model evaluation and selection framework, where you can cross-validate and perform various hyperparameter searches of models. You don’t ever want to question whether you chose the best model possible for the job, but Scikit-Learn makes it easy to affirm that you did.

What does TensorFlow do?

TensorFlow is another open-source framework maintained by Google for prototyping and evaluating machine learning models with a primary focus on neural networks. TensorFlow is built to be available in numerous languages—such as Python, Javascript, C++, Java, Go, and Swift—and has community-built support for many other languages.

TensorFlow is most commonly associated with neural networks, but in general, is highly optimized for any machine learning method that uses gradients (such as Boosted Trees). TensorFlow also includes a visualization tool known as TensorBoard to track and compare your trained models. 

The appeal of TensorFlow lies in its optimization and speed of neural networks. TensorFlow can run models on CPUs, GPUs, and even TPUs with an efficiency that few frameworks can match.

Scikit-Learn vs. TensorFlow: How Do They Compare?

Scikit-Learn and TensorFlow are both designed to help developers create and benchmark new models, so their functional implementations are quite similar with the key distinction that Scikit-Learn is used in practice with a wider scope of models as opposed to TensorFlow’s implied use for neural networks.

Scikit-Learn implements all of its machine learning algorithms as a base estimator and TensorFlow mirrors this terminology in its estimator class. Both frameworks’ estimators have abstract methods that are used by the framework to train and evaluate the estimator to ease head-to-head comparisons.

TensorFlow estimators and Scikit-Learn estimators are alike, but Scikit-Learn estimators are generally more flexible with other frameworks such as XGBoost, while TensorFlow estimators are intended to be built using TensorFlow core functionality which is optimized for neural networks.

Scikit-Learn does implement some barebones neural network models, but off-the-shelf doesn’t support more complex neural networks and the higher level of the customizability of TensorFlow.

In effect, Scikit-Learn often abstracts many of the details of the machine learning model away from the developer while the developer must implement details and inner-workings of their TensorFlow models. With this distinction comes a trade-off of speed, as the more flexible framework cannot achieve the performance of the specialized framework.

Scikit-Learn’s generality makes it useful for comparing entirely different types of machine learning models against each other; TensorFlow’s specialization enables under-the-hood optimizations, making it easier and more efficient to compare different TensorFlow and neural network models. For this reason, Scikit-Learn is often used to initially select the models you’ll later improve in greater detail.

TensorFlow’s availability in more languages and a greater focus on optimizations also makes it the preferred choice for deploying neural network models to production, as you can develop specifically for your target platform and squeeze out the greatest efficiency.

How can you use Scikit-Learn and TensorFlow together?

Since Scikit-Learn allows you to implement your own estimators, there’s nothing stopping you from using TensorFlow within Scikit-Learn’s framework to compare TensorFlow models against other Scikit-Learn models. This flexibility is extremely useful, as it allows you to determine whether to delve into the depths of TensorFlow models or to pursue a different Scikit-Learn model.

Both Scikit-Learn and TensorFlow are useful enough that they are likely to find a place in your development pipeline—but you must be mindful to use them to their advantages.

Is machine learning engineering the right career for you?

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Our machine learning training will teach you linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.

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