@@ -177,7 +177,7 @@ Let’s confirm this by comparing the prediction interval widths over
177177.. code :: python
178178
179179 fig, ax = plt.subplots(1 , 1 , figsize = (7 , 5 ))
180- for strategy in STRATEGIES .keys() :
180+ for strategy in STRATEGIES :
181181 ax.plot(X_test, y_preds[strategy][:, 2 ] - y_preds[strategy][:, 1 ])
182182 ax.axhline(1.96 * 2 * noise, ls = " --" , color = " k" )
183183 ax.set_xlabel(" x" )
@@ -320,7 +320,7 @@ strategies.
320320
321321 .. code :: python
322322
323- strategies = [' jackknife_plus' , ' jackknife_minmax' , ' cv_plus' , ' cv_minmax' ]
323+ strategies = [" jackknife_plus" , " jackknife_minmax" , " cv_plus" , " cv_minmax" ]
324324 n_figs = len (strategy)
325325 fig, axs = plt.subplots(2 , 2 , figsize = (13 , 12 ))
326326 coords = [axs[0 , 0 ], axs[0 , 1 ], axs[1 , 0 ], axs[1 , 1 ]]
@@ -353,7 +353,7 @@ Let's now compare the prediction interval widths between all strategies.
353353
354354 fig, ax = plt.subplots(1 , 1 , figsize = (7 , 5 ))
355355 ax.set_yscale(" log" )
356- for strategy in STRATEGIES .keys() :
356+ for strategy in STRATEGIES :
357357 ax.plot(X_test, y_preds[strategy][:, 2 ] - y_preds[strategy][:, 1 ])
358358 ax.axhline(1.96 * 2 * noise, ls = " --" , color = " k" )
359359 ax.set_xlabel(" x" )
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