.. _example_applications_plot_prediction_latency.py: ================== Prediction Latency ================== This is an example showing the prediction latency of various scikit-learn estimators. The goal is to measure the latency one can expect when doing predictions either in bulk or atomic (i.e. one by one) mode. The plots represent the distribution of the prediction latency as a boxplot. .. rst-class:: horizontal * .. image:: images/\plot_prediction_latency_001.png :scale: 47 * .. image:: images/\plot_prediction_latency_002.png :scale: 47 * .. image:: images/\plot_prediction_latency_003.png :scale: 47 * .. image:: images/\plot_prediction_latency_004.png :scale: 47 **Script output**:: Benchmarking SGDRegressor(alpha=0.01, average=False, epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.25, learning_rate='invscaling', loss='squared_loss', n_iter=5, penalty='elasticnet', power_t=0.25, random_state=None, shuffle=True, verbose=0, warm_start=False) Benchmarking RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, max_features='auto', max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False) Benchmarking SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto', kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False) benchmarking with 100 features benchmarking with 250 features benchmarking with 500 features example run in 6.54s **Python source code:** :download:`plot_prediction_latency.py ` .. literalinclude:: plot_prediction_latency.py :lines: 15- **Total running time of the example:** 6.54 seconds ( 0 minutes 6.54 seconds)