2 views

I am looking for a way to graph grid_scores_ from GridSearchCV in sklearn. In this example, I am trying to grid search for best gamma and C parameters for an SVR algorithm. My code looks as follows:

C_range = 10.0 ** np.arange(-4, 4)

gamma_range = 10.0 ** np.arange(-4, 4)

param_grid = dict(gamma=gamma_range.tolist(), C=C_range.tolist())

grid = GridSearchCV(SVR(kernel='rbf', gamma=0.1),param_grid, cv=5)

grid.fit(X_train,y_train)

print(grid.grid_scores_)

After I run the code and print the grid scores I get the following outcome:

[mean: -3.28593, std: 1.69134, params: {'gamma': 0.0001, 'C': 0.0001}, mean: -3.29370, std: 1.69346, params: {'gamma': 0.001, 'C': 0.0001}, mean: -3.28933, std: 1.69104, params: {'gamma': 0.01, 'C': 0.0001}, mean: -3.28925, std: 1.69106, params: {'gamma': 0.1, 'C': 0.0001}, mean: -3.28925, std: 1.69106, params: {'gamma': 1.0, 'C': 0.0001}, mean: -3.28925, std: 1.69106, params: {'gamma': 10.0, 'C': 0.0001},etc]

I would like to visualize all the scores (mean values) depending on gamma and C parameters. The graph I am trying to obtain should look as follows:

Where x-axis is gamma, the y-axis is the mean score (root mean square error in this case), and different lines represent different C values.

by (33.1k points)

Simply use the cv_results attribute.

For example:

def plot_grid_search(cv_results, grid_param_1, grid_param_2, name_param_1, name_param_2):

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')

plot_grid_search(pipe_grid.cv_results_, n_estimators, max_features, 'N Estimators', 'Max Features')

Output:

For more details, study Open Cv Machine Learning .