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. 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, we’ll compare TensorFlow and Scikit-Learn side-by-side to see what they do and how you can use them.Īfter 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.įrom 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?.