The code shown by @sascha is correct. However, the grid_scores_ attribute will be soon deprecated. It is better to use the cv_results attribute.
It can be implemente in a similar fashion to that of @sascha method:
def plot_grid_search(cv_results, grid_param_1, grid_param_2, name_param_1, name_param_2):
# Get Test Scores Mean and std for each grid search
scores_mean = cv_results['mean_test_score']
scores_mean = np.array(scores_mean).reshape(len(grid_param_2),len(grid_param_1))
scores_sd = cv_results['std_test_score']
scores_sd = np.array(scores_sd).reshape(len(grid_param_2),len(grid_param_1))
# Plot Grid search scores
_, ax = plt.subplots(1,1)
# Param1 is the X-axis, Param 2 is represented as a different curve (color line)
for idx, val in enumerate(grid_param_2):
ax.plot(grid_param_1, scores_mean[idx,:], '-o', label= name_param_2 + ': ' + str(val))
ax.set_title("Grid Search Scores", fontsize=20, fontweight="bold")
ax.set_xlabel(name_param_1, fontsize=16)
ax.set_ylabel('CV Average Score', fontsize=16)
ax.legend(loc="best", fontsize=15)
ax.grid('on')
# Calling Method
plot_grid_search(pipe_grid.cv_results_, n_estimators, max_features, 'N Estimators', 'Max Features')
The above results in the following plot:
