I have a dataset to analyze crypto prices against Tweet sentiment and I'm using random forest regression. Are the rates I'm getting good or bad? How do I interpret them?

CodePudding user response：

Your rmse is about 100 where the error is not big compare to average coin price 4400. I think you can work on to get more generalized or accurate prediction. Maybe you can validate your model with other data as well.

Yet it really depends on the goal you want. If the aim is to do HFT, 2% error would very huge. If your aim is to set RF model as base, I think it is a good way to start off.

Though it is a prediction task, it maybe necessary to check the correlation between Tweet and crypto price first so that you can be assured that there is enough statistical relationship between those 2 variables(correlation method for categorical vs interval variable may helpful).

CodePudding user response：

Mean absolute error is literally the average "distance" between your prediction and the "real" value. The mean squared error is the mean of the distance squared. and as you saw from your code the RMSE is the square root of the mean square error.

In the case of the MAE its usefull to "level" things. how? Percentage or fraction. MAE/np.mean(y_test) but depending on the data you are using you could use np.max(y_test) or np.min(y_test).

The MSE is less forgiving as it scales quadratically, so this basically will grow faster for every "unit of error".

As such, both the MSE and RMSE give more weight to larger errors. Normally you can compare RMSE scores between runs and improvements will be much more noticeable, I normally use RMSE as a scorer to minimize since when you use MAE, small deviations may be just part of the randomness in the RF.