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.

Lazypredict is an open-source python package created by Shankar Rao Pandala. Development and contribution to this are still going.

Properties of LazyPredict:

1. As of now, it is only based on Supervised learning algorithms(Regression and Classification)
2. Compatible with python version 3.6 and above.
3. Could be run on Command Line Interface(CLI).
4. Fast in predicting as all the basic model performances for the dataset is given at once.
5. Has an inbuilt Pipeline to scaling and transform the data and handle missing values and change categorical data to numeric.
6. Provides evaluation metrics on individual models.
7. Shows the time consumed by each model to build.

In this article, I’ll be discussing how to implement LazyPredict for regression and classification models with just a few lines of code.e.

#machine-learning