5 ways of AutoML in Pyhton

What is AutoML?

AutoML is one of the modern way of doing machine learning(ML), using libraries or tools that take away many hassles of data preprocessing, different model training, and testing. In most cases, we need to specify only what is our target variable and it is automatically taken care of by the AutoML library. 

In this post, we will discuss 5 of AutoML library for python.

AutoML Python

1. Lazypredict

Lazypredicts uses many basic models with minimum code and checks out which works better.It works for both classification and regression.

only works for python≥3.6

pip install lazypredict

Github: https://github.com/shankarpandala/lazypredict

2. AutoVIML

AutoVIML stands for “Automatically Build Variant Interpretable ML models”. This is an open source python package that helps to make many ML models faster for any dataset.

AutoVIML, cleans the data, performs feature reduction, can handle different types of data(e.g., text, number, date etc) for a single model.

 

pip install autoviml

Github:  https://github.com/AutoViML



3. Featurewiz

It’s a new python library that helps data scientist to find out the best features from that dataset faster using advance feature engineering method.

 

pip install featurewiz

Github:  https://github.com/AutoViML/featurewiz 

4. Auto_TS

It builds  and selects best time series model from ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code.

pip install auto-ts

Github:  https://github.com/AutoViML/Auto_TS

5. EvalML

Which is an AutoML library that builds, optimizes and evaluates machine learning pipelines. It’s an open source project which reduces time and complexity but in return provides accurate models with less effort. 

Model understanding of the model is found here using feature importance and permutation importance, partial dependence, precision-recall, confusion matrices, ROC curves, prediction explanations, and binary classifier threshold optimization etc.

pip install evalml

Github:  https://github.com/alteryx/evalml

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