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In vivo fluorometers use chlorophyllafluorescence (F_chl) as a proxy to monitor phytoplankton biomass. However, the fluorescence yield ofF_chlis affected by photoprotection processes triggered by increased irradiance (nonphotochemical quenching; NPQ), creating diurnal reductions inF_chlthat may be mistaken for phytoplankton biomass reductions. Published correction methods are mostly designed for pelagic oceans and are ill suited for inland waters or for high‐frequency data collection. A machine learning‐based method was developed to correct vertical profiler data from an oligotrophic lake. NPQ was estimated as a percent reduction inF_chlby comparing daytime values to mean, unquenched values from the previous night. A random forest regression was trained on sensor data collected coincident withF_chl; including solar radiation, water temperature, depth, and dissolved oxygen saturation. The accuracy of the model was assessed using a grouped 10‐fold cross validation (mean absolute error [MAE]: 7.6%; root mean square error [RMSE]: 10.2%), which was then used to correctF_chlprofiles. The model also predicted NPQ and corrected unseenF_chlprofiles from a future period with excellent results (MAE: 9.0%; RMSE: 14.4%).F_chlprofiles were then correlated to laboratory results, allowing corrected profiles to be compared directly to collected samples. The correction reduced error (RMSE) due to NPQ from 0.67 μg L⁻¹to 0.33 μg L⁻¹when compared to uncorrectedF_chldata. These results suggest that the use of machine learning models may be an effective way to correct for NPQ and may have universal applicability.