ATOM: A Python package for fast exploration of machine learning pipelines
During the exploration phase of a project, a data scientist tries
to find the optimal pipeline for his specific use case. This usually
involves applying standard data cleaning steps, creating or selecting
useful features, trying out different models, etc. Testing multiple
pipelines requires many lines of code, and writing it all in the same
notebook...
How to test multiple machine learning pipelines with just a few lines of Python
Since it's nearly impossible to know beforehand which transformations
will benefit the model's outcome the most, this process usually involves
trying out different approaches. For example, if we are dealing with
an imbalanced dataset, should we oversample the minority class or
undersample the majority...
From raw data to web app deployment with ATOM and Streamlit
In this article we will show you how to create a simple web app,
capable of helping a data scientist to quickly perform a basic
analysis on the performance of predictive models on a provided
dataset. The user will be able to upload his own dataset and tweak
the machine learning pipeline in two ways: selecting which data
cleaning steps to apply...
Exploration of Deep Learning pipelines made easy
During the exploration phase of a project, a data scientist tries
to find the optimal pipeline for his specific use case. In this
story, I’ll explain how to use the ATOM package to quickly help you
train and evaluate a deep learning model on any given dataset...
Deep Feature Synthesis vs Genetic Feature Generation
Feature engineering is the process of creating new features from the
existing ones, in order to capture relationships with the target
column that the first set of features didn't have on their own. This
process is very important to improve the performance of machine...
From raw text to model prediction in under 50 lines of Python