As an HR leader/manager, how do you define success? Are you sure that definition will translate to the best recruiting algorithm? I've thought about this a lot, and in this post (part of a blog series) I share my favorite guidelines for crafting the best possible target when designing a recruiting algorithm.
Part 1 of a blog series explaining and addressing various considerations for designing algorithms that aid HR teams in recruiting practices, and using DataRobot's automation features to build and test models quickly.
To illustrate how machine learning can aid Propensity Score Matching (PSM) through support of quasi-experimentation, I created two Zepl notebooks. This post explains how these notebooks give you an end-to-end demo of using DataRobot for PSM.
Together, DataRobot and Zepl will drive productivity, efficiency, and collaboration for multiple personas through integration of cloud-native, self-service notebook in the DataRobot enterprise AI platform.
Using the DataRobot R client package and historical data from the U.S., France, and Spain, I created an Automated Time Series model that illustrates the effectiveness of lockdowns. Check it out and try it out yourself!
You can find a new tutorial on how to replicate the predicting COVID-19 model at the county level using DataRobot here.
This post includes a video walkthrough on how to create these models in the GUI, as well as a Python notebook for achieving this programatically. You can also find the dataset attached, as well as some advice on data sources for COVID-19.
Please feel free to post any questions you might have in Community. We'd also love to hear how you are modeling for COVID-19.
Here, I explain what target leakage is, show you an example, provide a few suggestions to avoid potential leakage, and then explain the target leakage detection support built into the DataRobot platform.
Need a Tip? DataRobot experts are putting together some helpful DataRobot usage tips for the platform, trial, features, etc. You can find these easily in the Tip of the Day board (under Read). Let us know if you've found a good one or have a good one to add!