after converting it to pandas dataframein[ ]import pandas as pd convert to dataframe results pd dataframe(grid_search cv_results_show the first rows display(results head()out[ ] param_c param_gamma rank_test_score split _test_score params {' ' 'gamma' {' ' 'gamma' {' ' 'gamma' {' ' 'gamma' {' ' 'gamma' split _test_score mean_test_score split _test_score split _test_score split _test_score std_test_score each row in results corresponds to one particular parameter setting for each settingthe results of all cross-validation splits are recordedas well as the mean and standard deviation over all splits as we were searching two-dimensional grid of parameters ( and gamma)this is best visualized as heat map (figure - first we extract the mean validation scoresthen we reshape the scores so that the axes correspond to and gammain[ ]scores np array(results mean_test_scorereshape( plot the mean cross-validation scores mglearn tools heatmap(scoresxlabel='gamma'xticklabels=param_grid['gamma']ylabel=' 'yticklabels=param_grid[' ']cmap="viridis" model evaluation and improvement