You donвЂ™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. 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. 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. 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. Scikit-Learn is an open-source package for creating and evaluating machine learning models of all flavors in Python. *Looking for the Colab Notebook for this post? Find it right here.* What is Scikit-Learn used for? This is where TensorFlow and Scikit-Learn can help you.

In this article, weвЂ™ll compare TensorFlow and Scikit-Learn side-by-side to see what they do and how you can use them.ДЄfter reading an exciting paper or cleaning your data, whatвЂ™s the next step? You want to start building your machine learning models and testing them-after all, thatвЂ™s the exciting part of machine learning.ДЇrom 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. How can you use Scikit-Learn and TensorFlow together?.

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