Do certain machine learning algorithms have higher variance in their predictions than others or have parameters to adjust this? Right now I'm using a regularized linear model for time series prediction on very noisy data and it's resulting in very conservative predictions out of sample i.e. predicting close to the mean value for most observations. I'd like to have a model that has high variance in it's predictions and either hits or misses rather than hits in the middle every time. Are there certain models I should try or tips I could try?
Yes,Certain machine learning algorithms have higher variance. A good rule of thumb is the more parameters a model has the higher the variance of that model.
Trees have high variance, you reduce the variance by ensembles i.e random forests.
Neural networks also have higher variance, if you don't use regularization or drop out layers.
You can also map your data into a higher dimensional space using polynomials as well.