EXA Fix iterative imputer example's speed issue. (#13379)

examples/impute/plot_iterative_imputer_variants_comparison.py

@@ -57,6 +57,10 @@
 rng = np.random.RandomState(0)
 
 X_full, y_full = fetch_california_housing(return_X_y=True)
+# ~2k samples is enough for the purpose of the example.
+# Remove the following two lines for a slower run with different error bars.
+X_full = X_full[::10]
+y_full = y_full[::10]
 n_samples, n_features = X_full.shape
 
 # Estimate the score on the entire dataset, with no missing values
@@ -93,7 +97,7 @@
 estimators = [
     BayesianRidge(),
     DecisionTreeRegressor(max_features='sqrt', random_state=0),
-    ExtraTreesRegressor(n_estimators=10, n_jobs=-1, random_state=0),
+    ExtraTreesRegressor(n_estimators=10, random_state=0),
     KNeighborsRegressor(n_neighbors=15)
 ]
 score_iterative_imputer = pd.DataFrame()