![Comparison of the ease of use and accuracy of two machine learning algorithms – forestry case study](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBdVVIIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--105b779c8dceaec82fcb23f1a4588c6a643a15ef/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJYW5CbkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--a3b53ba1a0f83efef18f6e75a8d4ce784384bee2/30212411048_96d9eea677_o.jpg)
Machine learning algorithms are becoming increasingly popular for data crunching across a vast area of scientific disciplines. Here, the authors compare two machine learning algorithms with respect to accuracy and user-friendliness and find that random forest algorithms outperform logistic regression when applied to the same dataset.
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