modern computers do)you can use the n_jobs parameter to adjust the number of cores to use using more cpu cores will result in linear speed-ups (using two coresthe training of the random forest will be twice as fast)but specifying n_jobs larger than the number of cores will not help you can set n_jobs=- to use all the cores in your computer you should keep in mind that random forestsby their natureare randomand setting different random states (or not setting the random_state at allcan drastically change the model that is built the more trees there are in the forestthe more robust it will be against the choice of random state if you want to have reproducible resultsit is important to fix the random_state random forests don' tend to perform well on very high dimensionalsparse datasuch as text data for this kind of datalinear models might be more appropriate random forests usually work well even on very large datasetsand training can easily be parallelized over many cpu cores within powerful computer howeverrandom forests require more memory and are slower to train and to predict than linear models if time and memory are important in an applicationit might make sense to use linear model instead the important parameters to adjust are n_estimatorsmax_featuresand possibly pre-pruning options like max_depth for n_estimatorslarger is always better averaging more trees will yield more robust ensemble by reducing overfitting howeverthere are diminishing returnsand more trees need more memory and more time to train common rule of thumb is to build "as many as you have time/memory for as described earliermax_features determines how random each tree isand smaller max_features reduces overfitting in generalit' good rule of thumb to use the default valuesmax_features=sqrt(n_featuresfor classification and max_fea tures=log (n_featuresfor regression adding max_features or max_leaf_nodes might sometimes improve performance it can also drastically reduce space and time requirements for training and prediction gradient boosted regression trees (gradient boosting machinesthe gradient boosted regression tree is another ensemble method that combines multiple decision trees to create more powerful model despite the "regressionin the namethese models can be used for regression and classification in contrast to the random forest approachgradient boosting works by building trees in serial mannerwhere each tree tries to correct the mistakes of the previous one by defaultthere is no randomization in gradient boosted regression treesinsteadstrong pre-pruning is used gradient boosted trees often use very shallow treesof depth one to fivewhich makes the model smaller in terms of memory and makes predictions faster supervised learning