title: How to get independent probabilities of all classes for each sample with predict_proba?
body: <p>In my work, there is a feature set consisting entirely of Boolean data and there are classes belonging to the features. Classes are string.</p>
  <pre><code>     feature set              class (String)
  [True False True   ...]        &quot;A&quot;
  [True True  True   ...]        &quot;B&quot;
  [True True  False   ...]       &quot;C&quot;
  </code></pre>
  <p>When I train these data with the Random Forest algorithm,</p>
  <pre><code>factor = pd.factorize(classes)
  classes = factor[0]
  
  classifier = RandomForestClassifier(n_estimators=100, criterion=&quot;entropy&quot;, random_state=0)
  classifier.fit(x_train, classes)
  </code></pre>
  <p>classifier can detect 97% of classes correctly. When I do</p>
  <pre><code>classifier.predict_proba(sample1_feature_set)
  </code></pre>
  <p>it gives relative probabilities of each class for sample1. For example; Like</p>
  <pre><code> [0.80    0.05    0.15]
     ↓        ↓        ↓
    Prob.    Prob.    Prob.
     of       of       of
    &quot;A&quot;      &quot;B&quot;      &quot;C&quot; 
    for      for      for
  sample1   sample1  sample1
  </code></pre>
  <p>so when I add the values of list (0.80 + 0.05 + 0.15), the result is always 1. This shows that it actually makes relative evaluation, that is the probability of one class affects the probability of the other class.</p>
  <p>I want to get the independent probabilities of all classes for sample1, like</p>
  <pre><code> [0.95    0.69    0.87]
     ↓        ↓        ↓
    Prob.    Prob.    Prob.
     of       of       of
    &quot;A&quot;      &quot;B&quot;      &quot;C&quot; 
    for      for      for
  sample1   sample1  sample1
  </code></pre>
  <p>Sample1 is %95 of &quot;A&quot;, %69 of &quot;B&quot; and %87 of &quot;C&quot; class. Do you have any idea how I can do this?</p>