title: "purposed" imbalanced data and class_weight in keras
body: <p>I have 4 classes, namely <strong>a</strong>, <strong>b</strong>, <strong>c</strong>, <strong>d</strong>. With training data as follows (approx):</p>
  
  <p><strong>a</strong>: 1500
  <strong>b</strong>: 500
  <strong>c</strong>: 500
  <strong>d</strong>: 500</p>
  
  <p>So class <strong>a</strong> has a sample count 3 times to <strong>b</strong>, <strong>c</strong> and <strong>d</strong></p>
  
  <p>In real case scenario, the probability of emergence of <strong>a</strong> could be much higher than <strong>b</strong>,<strong>c</strong>,<strong>d</strong>. And if the model has difficulty in making decisions, I also want it to select <strong>a</strong>. </p>
  
  <p>In this case, in the <strong>fit_generator</strong> function of keras, do I need to specify the <strong>class_weight</strong> parameter ?</p>
  
  <p>Many thanks !</p>