How to graph grid scores from GridSearchCV?

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:

from sklearn.svm import SVCfrom sklearn.grid_search import GridSearchCVfrom sklearn import datasetsimport matplotlib.pyplot as pltimport seaborn as snsimport numpy as npdigits = datasets.load_digits()X = digits.datay = digits.targetclf_ = SVC(kernel='rbf')Cs = [1, 10, 100, 1000]Gammas = [1e-3, 1e-4]clf = GridSearchCV(clf_,            dict(C=Cs,                 gamma=Gammas),                 cv=2,                 pre_dispatch='1*n_jobs',                 n_jobs=1)clf.fit(X, y)scores = [x[1] for x in clf.grid_scores_]scores = np.array(scores).reshape(len(Cs), len(Gammas))for ind, i in enumerate(Cs):    plt.plot(Gammas, scores[ind], label='C: ' + str(i))plt.legend()plt.xlabel('Gamma')plt.ylabel('Mean score')plt.show()

• Code is based on this.
• Only puzzling part: will sklearn always respect the order of C & Gamma -> official example uses this "ordering"

Output:

For plotting the results when tuning several hyperparameters, what I did was fixed all parameters to their best value except for one and plotted the mean score for the other parameter for each of its values.

def plot_search_results(grid):    """    Params:         grid: A trained GridSearchCV object.    """    ## Results from grid search    results = grid.cv_results_    means_test = results['mean_test_score']    stds_test = results['std_test_score']    means_train = results['mean_train_score']    stds_train = results['std_train_score']    ## Getting indexes of values per hyper-parameter    masks=[]    masks_names= list(grid.best_params_.keys())    for p_k, p_v in grid.best_params_.items():        masks.append(list(results['param_'+p_k].data==p_v))    params=grid.param_grid    ## Ploting results    fig, ax = plt.subplots(1,len(params),sharex='none', sharey='all',figsize=(20,5))    fig.suptitle('Score per parameter')    fig.text(0.04, 0.5, 'MEAN SCORE', va='center', rotation='vertical')    pram_preformace_in_best = {}    for i, p in enumerate(masks_names):        m = np.stack(masks[:i] + masks[i+1:])        pram_preformace_in_best        best_parms_mask = m.all(axis=0)        best_index = np.where(best_parms_mask)[0]        x = np.array(params[p])        y_1 = np.array(means_test[best_index])        e_1 = np.array(stds_test[best_index])        y_2 = np.array(means_train[best_index])        e_2 = np.array(stds_train[best_index])        ax[i].errorbar(x, y_1, e_1, linestyle='--', marker='o', label='test')        ax[i].errorbar(x, y_2, e_2, linestyle='-', marker='^',label='train' )        ax[i].set_xlabel(p.upper())    plt.legend()    plt.show()