be done completely independently from the other parameter settings and models this makes grid search and cross-validation ideal candidates for parallelization over multiple cpu cores or over cluster you can make use of multiple cores in grid searchcv and cross_val_score by setting the n_jobs parameter to the number of cpu cores you want to use you can set n_jobs=- to use all available cores you should be aware that scikit-learn does not allow nesting of parallel operations soif you are using the n_jobs option on your model (for examplea random forest)you cannot use it in gridsearchcv to search over this model if your dataset and model are very largeit might be that using many cores uses up too much memoryand you should monitor your memory usage when building large models in parallel it is also possible to parallelize grid search and cross-validation over multiple machines in clusteralthough at the time of writing this is not supported within scikit-learn it ishoweverpossible to use the ipython parallel framework for parallel grid searchesif you don' mind writing the for loop over parameters as we did in "simple grid searchon page for spark usersthere is also the recently developed spark-sklearn packagewhich allows running grid search over an already established spark cluster evaluation metrics and scoring so farwe have evaluated classification performance using accuracy (the fraction of correctly classified samplesand regression performance using howeverthese are only two of the many possible ways to summarize how well supervised model performs on given dataset in practicethese evaluation metrics might not be appropriate for your applicationand it is important to choose the right metric when selecting between models and adjusting parameters keep the end goal in mind when selecting metricyou should always have the end goal of the machine learning application in mind in practicewe are usually interested not just in making accurate predictionsbut in using these predictions as part of larger decisionmaking process before picking machine learning metricyou should think about the high-level goal of the applicationoften called the business metric the consequences of choosing particular algorithm for machine learning application are evaluation metrics and scoring