n_features cancer data shape[ plt barh(range(n_features),forest feature_importances_align='center'plt yticks(np arange(n_features),cancer feature_namesplt xlabel('feature importance'plt ylabel('feature'plt show(performance of classifier after implementing machine learning algorithmwe need to find out how effective the model is the criteria for measuring the effectiveness may be based upon datasets and metric for evaluating different machine learning algorithmswe can use different performance metrics for examplesuppose if classifier is used to distinguish between images of different objectswe can use the classification performance metrics such as average accuracyaucetc in one or other sensethe metric we choose to evaluate our machine learning model is very important because the choice of metrics influences how the performance of machine learning algorithm is measured and compared following are some of the metricsconfusion matrix basically it is used for classification problem where the output can be of two or more types of classes it is the easiest way to measure the performance of classifier confusion matrix is basically table with two dimensions namely "actualand "predictedboth the dimensions have "true positives (tp)""true negatives (tn)""false positives (fp)""false negatives (fn)