Question: Please use R - Squared as evaluation metric. 1 feature selection method. 2 machine learning algorithms ( different types, no models ) . Analysis and
Please use RSquared as evaluation metric.
feature selection method.
machine learning algorithms different types, no models
Analysis and results
Preliminary conclusions and ideas to further improve your model for the contest.
Additional feature selection methods even those not reviewed in class
Other feature engineering techniques
Crossvalidation
Additional machine learning algorithms even those not reviewed in class
Hyperparameter tuning
Learning curvesDataset Description: The dataset is composed of several nextday forecast variables, maximum and
minimum temperatures of presentday, and geographic auxiliary variables collected for a period of
years by the Korean Meteorological Service over Seoul, South Korea. The output variable is the nextday
average temperature NextDayAvTemp
Dataset Variables:
Station: Used weather station number.
Present Tmax: Maximum air temperature between and on the present day
Present Tmin: Minimum air temperature between and on the present day
NextDayPred RHmin: Forecast of nextday minimum relative humidity
NextDayPredRHmax: Forecast of nextday maximum relative humidity
NextDayPredTmax lapse: Forecast of nextday maximum air temperature applied lapse rate
NextDayPredImin lapse: Forecast of nextday minimum air temperature applied lapse rate
NextDayPredWS: Forecast of nextday average wind speed
NextDayPredLH: Forecast of nextday average latent heat flux Wm
NextDayPredCC: Forecast of nextday st hour split average cloud cover
NextDayPredCC: Forecast of nextday nd hour split average cloud cover h
NextDayPredCC: Forecast of nextday rd hour split average cloud cover h
NextDayPredCC: Forecast of nextday th hour split average cloud cover h
NextDayPredPPT: Forecast of nextday st hour split average precipitation h
NextDayPred
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