from sklearn metrics scorer import scorers print("available scorers:\ {}format(sorted(scorers keys()))out[ ]available scorers['accuracy''adjusted_rand_score''average_precision'' '' _macro'' _micro'' _samples'' _weighted''log_loss''mean_absolute_error''mean_squared_error''median_absolute_error''precision''precision_macro''precision_micro''precision_samples''precision_weighted'' ''recall''recall_macro''recall_micro''recall_samples''recall_weighted''roc_auc'summary and outlook in this we discussed cross-validationgrid searchand evaluation metricsthe cornerstones of evaluating and improving machine learning algorithms the tools described in this together with the algorithms described in and are the bread and butter of every machine learning practitioner there are two particular points that we made in this that warrant repeatingbecause they are often overlooked by new practitioners the first has to do with cross-validation cross-validation or the use of test set allow us to evaluate machine learning model as it will perform in the future howeverif we use the test set or cross-validation to select model or select model parameterswe "use upthe test dataand using the same data to evaluate how well our model will do in the future will lead to overly optimistic estimates we therefore need to resort to split into training data for model buildingvalidation data for model and parameter selectionand test data for model evaluation instead of simple splitwe can replace each of these splits with cross-validation the most commonly used form (as described earlieris training/test split for evaluationand using cross-validation on the training set for model and parameter selection the second point has to do with the importance of the evaluation metric or scoring function used for model selection and model evaluation the theory of how to make business decisions from the predictions of machine learning model is somewhat beyond the scope of this book howeverit is rarely the case that the end goal of machine learning task is building model with high accuracy make sure that the metric you choose to evaluate and select model for is good stand-in for what the model will actually be used for in realityclassification problems rarely have balanced classesand often false positives and false negatives have very different consequences we highly recommend foster provost and tom fawcett' book data science for business ( 'reillyfor more information on this topic model evaluation and improvement