Optimizing hyperparameters for machine learning models is a key step in making accurate predictions. Hyperparameters define characteristics of the model that can impact model accuracy and computational efficiency. They are typically set prior to fitting the model to the data. In contrast, parameters are values estimated during the training process... Read more

Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detecting the Higgs-Boson particle, machine learning excels at extracting knowledge from large amounts of data. This talk gives a general introduction to machine learning and introduces practical tools for... Read more

Ted will present more on this topic at ODSC East 2019 this May in his presentation, “Integrating Pandas with Scikit-Learn, an Exciting New Workflow“ This article is available as a Jupyter Notebook on Google’s Colaboratory (open in playground mode to run and edit) and at the Machine Learning Github repository for the Dunder Data Organization.... Read more

In my previous post, we constructed a simple cross-validated regression model using Scikit-Learn in 35 lines. It’s pretty amazing that we can perform machine learning with so little effort, but we just did the bare minimum in order to get a working model. Frankly, it didn’t even perform that well. What... Read more

Scikit-Learn is one of the premier tools in the machine learning community, used by academics and industry professionals alike. At ODSC East 2019, Scikit-Learn author Andreas Mueller will host a training session to give beginners a crash course —this is your guide to scikit-learn. As one of the primary contributors... Read more

As you encounter various data elements you should come across categorical data. Some individuals simply discard this data in their analysis or do not bring it into their models. That is certainly an option, however many times the categorical data represents information that we would typically want to bring in to these... Read more

In the Introductory article about random forest algorithm, we addressed how the random forest algorithm works with real life examples. As continues to that, In this article we are going to build the random forest algorithm in python with the help of one of the best Python machine learning libraryScikit-Learn. To build the random forest... Read more

The ODSC Team was thrilled to present scikit-learn the Outstanding Data Science Project award, East, in Boston on May 5th.  Scikit-learn has been instrumental in making high quality machine learning algorithms more accessible to countless data scientists, students and practitioners.  As an active ongoing project it has made a tremendous... Read more

What’s a Large Data Set? A data set is said to be large when it exceeds 20% of the available RAM for a single machine. Which for your standard MacBook Pro with 8Gb of RAM, corresponds to a meager 2Gb dataset — size that is becoming more and more frequent... Read more

Editor’s note: Franz Kiraly is a speaker for ODSC Europe this June. Be sure to check out his talk, “sktime – Python Toolbox for Machine Learning with Time Series,” there! Welcome to sktime, the open community and Python framework for all things time series. Here’s what you need to know:... Read more