<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>

CC BY-SA 4.0
2020-11-02T05:15:21.797Z
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 $i$?
Neil Slater
CC BY-SA 4.0
2020-11-15T00:12:09.427Z
All $i$ in this case
yupbank
<machine-learning><performance><ensemble-learning>
Would performance of atomic models matter in ensemble methods?
yupbank
# 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

