title: What is the numpy way to conditionally merge arrays?
body: <p>I have two numpy arrays <code>(1000,)</code> filled with predictions from two models:</p>
  
  <pre><code>pred_1 = model_1.predict(x_test)
  pred_2 = model_2.predict(x_test)
  </code></pre>
  
  <p><code>model_1</code> is attractive due to extremely low <code>FP</code>, but consequently high <code>FN</code>.</p>
  
  <p><code>model_2</code> is attractive due to overall accuracy and recall.</p>
  
  <p>How can I <em>conditionally</em> apply predictions to take advantage of these strengths and weaknesses?</p>
  
  <p>I'd like to take all positive (<code>1</code>) predictions from the first model, and let the second model deal with the rest.</p>
  
  <p>Essentially I'm looking for something like this:</p>
  
  <pre><code>final_pred = model_1.predict() if model_1.predict() &gt; 0.5 else model_2.predict()
  </code></pre>
  
  <p>This fails: The truth value of an array with more than one element is ambiguous.</p>
  
  <p>What is the numpy way to combine these arrays as above?</p>