Body: <p>Suppose I have two fitted ensemble models <span class="math-container">$F_1 := (f_1, f_2, f_3, \cdots f_n)$</span> and <span class="math-container">$G_1 := (g_1, g_2, g_3, \cdots g_n)$</span>.</p>
  <p>And they were using the same ensemble methods (boosting or bagging).</p>
  <p>And I am using some measurement for model performance <span class="math-container">$M: f_i \to \mathbb{R}^+$</span>, higher the better.</p>
  <p>And I know beforehand <span class="math-container">$M(f_i) \gt M(g_i), \forall i \in [1,n]$</span>, can I conclude <span class="math-container">$M(F) \gt M(G) $</span> ?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-02T05:15:21.797Z
Comments.Text: Do you know $M(f_i) \gt M(g_i), \forall i \in [1,n]$ or is $M(f_i) \gt M(g_i)$ only for a specific $i$?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-15T00:12:09.427Z
Comments.Text: All $i$ in this case
Comments.UserDisplayName: yupbank
Tags: <machine-learning><performance><ensemble-learning>
Title: Would performance of atomic models matter in ensemble methods?
UserDisplayName: yupbank
ThreadText: # Would performance of atomic models matter in ensemble methods?
  
  (Asked by: username_0 on 2020-10-08)
  
  Suppose I have two fitted ensemble models $F\_1 := (f\_1, f\_2, f\_3, \cdots f\_n)$ and $G\_1 := (g\_1, g\_2, g\_3, \cdots g\_n)$.
  
  And they were using the same ensemble methods (boosting or bagging).
  
  And I am using some measurement for model performance $M: f\_i \to \mathbb{R}^+$, higher the better.
  
  And I know beforehand $M(f\_i) \gt M(g\_i), \forall i \in [1,n]$, can I conclude $M(F) \gt M(G) $ ?
  
  **Tags:** machine-learning, performance, ensemble-learning
  > **Comments:**
  > **username_1**: Do you know $M(f_i) \gt M(g_i), \forall i \in [1,n]$ or is $M(f_i) \gt M(g_i)$ only for a specific $i$?  
  > **username_0**: All $i$ in this case  
  
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  ## 0 answers
  
  