I am training a random forest model with following parameters. n_estimators=50,criterion='mse',max_depth=None,min_samples_split=2, min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features='auto',max_leaf_nodes=None,oob_score=True My data set consists of 5258 rows and columns contain a categorical variable , 36 temperature data and 36 precipitation data. The model gives a R^2 score 0.81 on train data set where as only 0.35 on test data set. i have divided train and test data in 90:10 ratio.
Can i have suggestions how to improve the score?