<p>given H a family of functions which is PAC lernable such that for <span class="math-container">$\epsilon$</span> error and  <span class="math-container">$\delta $</span> confidence interval it required <span class="math-container">$m(\epsilon,\delta)$</span> samples.</p>
<p>I understood that if we learn H under agnostic-PAC the same function will require more samples for the same accuracy meaning:</p>
<p><span class="math-container">$m_{agnostic}(\epsilon,\delta)&gt;m(\epsilon,\delta)$</span></p>
<p>since H is PAC learnable we know that H contain a realizable solution with 0 error, so why is the number of samples still larger for agnostic -PAC?</p>

CC BY-SA 4.0
2023-08-01T19:48:42.710Z
"since H is PAC learnable we know that H contain a realizable solution with 0 error" 

no. in the agnostic setup, you have distributions under which no hypothesis in H is realizable (i.e., with 0 error).
mathworker21
CC BY-SA 4.0
2023-08-06T13:22:28.097Z
thank you for answering, so as i understand the exitance of a realizable solution is unrelatable to the assumed distribution of sample, prediction pairs.?
Tomer Gigi
<machine-learning><pac-learning>
Learning a PAC-lernable using agnostic-PAC framework
Tomer Gigi
# Learning a PAC-lernable using agnostic-PAC framework

(Asked by: username_0 on 2023-08-01)

given H a family of functions which is PAC lernable such that for $\epsilon$ error and $\delta $ confidence interval it required $m(\epsilon,\delta)$ samples.

I understood that if we learn H under agnostic-PAC the same function will require more samples for the same accuracy meaning:

$m\_{agnostic}(\epsilon,\delta)>m(\epsilon,\delta)$

since H is PAC learnable we know that H contain a realizable solution with 0 error, so why is the number of samples still larger for agnostic -PAC?

**Tags:** machine-learning, pac-learning

> **Comments:**
> **username_1**: "since H is PAC learnable we know that H contain a realizable solution with 0 error"   no. in the agnostic setup, you have distributions under which no hypothesis in H is realizable (i.e., with 0 error).  
> **username_0**: thank you for answering, so as i understand the exitance of a realizable solution is unrelatable to the assumed distribution of sample, prediction pairs.?  

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