Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake

Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model

“…With the continuous progress of technology and methods, data mining technology will bring about the transformation of research methods in the field of marketing and consumer behavior from deduction to induction [ 33 ]. In solving the research of consumer behavior mining, more researchers began to study customer lifetime value and its components based on machine learning algorithms, such as establishing customer churn prediction models through machine learning methods, such as classification tree [ 34 ], GAM [ 35 , 36 ], SVM [ 37 ], and random forest [ 21 ]. Chen and Fan [ 38 ] used support vector regression to predict dynamic customer lifetime value and used customer behavior variables and controlled variables about multiple promotions to select the best promotion.…”

Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning

“…They mention Chlorophyll a, water pH, water temperature, water alkalinity, chloride, etc. are the factors the affect the biomass availability [7]. Hoi, et al, discuss that light factors and nitrogen availability alters biomass and C-phycocyanin productivity of Thermosynechococcus [8].…”

Environmental, geographical and time‐related impacts on avian malaria infections in native and introduced populations of house sparrows (Passer domesticus), a globally invasive species

“…Conversely, RF models do not assume any specific probability distribution for the variables, and can include potentially complex interactions between all covariates (Zhang et al, 2021).…”