I owe an apology to the R Project for Statistical Computing. In last week’s blog on R and Python, I conjectured that Python statistical learning functions may, in many cases, perform better than their R counterparts, noting that “I use the Python versions of gradient boosting and random forests on models of a half million cases without hesitation, while I can’t recall running an equivalent R model on N > 200,000 with impunity.” While the Python scikit-learn modules do appear to perform well, I hadn’t at the time re-calibrated the latest version of R on my new 16 GB RAM, high-end processor Wintel notebook. It turns out there’s quite a boost in performance from both hardware and software.
All Information Management articles are archived after 7 days. REGISTER NOW for unlimited access to all recently archived articles, as well as thousands of searchable stories. Registered Members also gain access to:
- Full access to information-management.com including all searchable archived content
- Exclusive E-Newsletters delivering the latest headlines to your inbox
- Access to White Papers, Web Seminars, and Blog Discussions
- Discounts to upcoming conferences & events
- Uninterrupted access to all sponsored content, and MORE!