Evaluating machine learning algorithms is a common task performed by data scientists. While a data scientist needs to know the different types of machine learning algorithms to use for different types of problems, it is nevertheless paramount that he puts the different algorithms to work on his/her specific dataset. Only by doing that would he/she have a better sense of which algorithm to use to train the model and how to perform hyper-parameter tuning after that. However, choosing the right algorithms is a time-consuming and exhausting process. Ideally, there should be an automated process where you just need to supply your data and the ideal machine learning algorithm to use would be chosen for you. The answer to this is LazyPredict.

We all have come around or will come around to this situation: when we try to find which machine learning model will work best on the given dataset? To find this answer, you need to write code for various ml models and train your dataset on each and every model, then try to compare the performance of these models on the test dataset. Well, here is the solution to your problem: Lazypredict. LazyPredict is a powerful Python library for machine learning that provides an easy-to-use and convenient way to compare various ml models all at once. It is designed to be a flexible, intuitive, and easy-to-use library that can be used for both classification and regression tasks.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Data scientists have to prioritize.

Some of my most popular blogs on Medium are about libraries that I believe you should try. In this blog, I will focus on low-code machine learning libraries. The truth is that many data scientists believe that low-code libraries are shortcuts and should be avoided. I'm afraid I have to disagree! I think that low-code libraries should be included in our pipeline to help us make important decisions without wasting time.

Some of my most popular blogs on Medium are about libraries that I believe you should try. In this blog, I will focus on low-code machine learning libraries. The truth is that many data scientists believe that low-code libraries are shortcuts and should be avoided. I'm afraid I have to disagree! I think that low-code libraries should be included in our pipeline to help us make important decisions without wasting time.

While building machine learning models we are not sure which algorithm should work well with the given dataset, hence we end up trying many models and keep iterating until we get proper accuracy. Have you ever thought about getting all the basic algorithms at once to predict for model performance? LazyPredict is a module helpful for this purpose. LazyPredict will generate all the basic machine learning algorithms' performances on your model. Along with the accuracy score, LazyPredict provides certain evaluation metrics and the time taken by each model.