<p>In Boosting, we improve the overall metrics of the model by sequentially building weak models and then building upon the weak metrics of previous models.</p>
<p>We start out by applying basic non-specific algorithms to the problem, which returns some weak prediction functions by taking arbitrary solutions (like sparse weights or assigning equal weights/attention). We improve upon this in the following predictions by adjusting weights to those having a higher error rate. After going through many iterations, we combine it to create a single Strong Prediction Function which has better metrics.</p>
<hr />
<p>Some popular Boosting Algorithms :</p>
<ul>
<li>AdaBoost</li>
<li>Gradient Tree Boosting</li>
<li>XGBoost</li>
</ul>

CC BY-SA 4.0
2020-09-27T10:35:18.053Z
What are "weak prediction functions" and "Strong Prediction Function"? These terms are ambiguous because you did not define what you mean by "weak" and "strong" and you also have not defined "prediction function". Also, what do you mean by "weak metrics"? Do you mean metrics according to which the model performs bad?
nbro
CC BY-SA 4.0
2020-09-27T03:02:01.313Z
2020-09-27T03:02:01.313Z
Saurav Maheshkar
<p>You take a bunch of weak learners, each of them trained on a subset of the data.</p>
<p>You then just get all of them to make a prediction, and you learn how much you can trust each one, resulting in a weighted vote or other type of combination of the individual predictions.</p>

CC BY-SA 4.0
2020-09-26T20:09:16.093Z
2020-09-26T20:09:16.093Z
Robby Goetschalckx
<p>As @desertnaut mentioned in the comment</p>
<blockquote>
<p>No weak learner becomes strong; it is the ensemble of the weak learners that turns out to be strong</p>
</blockquote>
<p>Boosting is an ensemble method that integrates multiple models(called as weak learners) to produce a supermodel (Strong learner).</p>
<p><a href="https://i.sstatic.net/tdxd7.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/tdxd7.png" alt="Boosting technique is illustrated" /></a></p>
<p>Basically boosting is to train weak learners sequentially,  each trying to correct its predecessor. For boosting, we need to specify a weak model (e.g. regression, shallow decision trees, etc.), and then we try to improve each weak learner to learn something from the data.</p>
<p><em>AdaBoost</em> is a boosting algorithm where a decision tree with a single split is used as a weak learner. Also, we have <em>gradient boosting</em> and <em>XG boosting</em>.</p>

CC BY-SA 4.0
2020-09-27T10:33:45.447Z
You say "and then improves it.", who improves what?
nbro
CC BY-SA 4.0
2020-09-29T02:32:54.530Z
we try to improve each weak learner to learn something from the data
Sivaram Rasathurai
CC BY-SA 4.0
2020-09-27T03:00:37.663Z
2020-09-29T02:31:58.360Z
2020-09-29T02:31:58.360Z
Sivaram Rasathurai
<p><em>Boosting</em> refers to a family of algorithms which converts <em>weak learners</em> to <em>strong learners</em>. How does it happen?</p>

CC BY-SA 4.0
2020-09-26T23:41:03.647Z
No weak learner becomes strong; it is the *ensemble* of the weak learners that turns out to be strong.
desertnaut
<machine-learning><gradient-boosting><boosting>
How do weak learners become strong in boosting?
Legend
# How do weak learners become strong in boosting?

(Asked by: username_0 on 2020-09-26)

*Boosting* refers to a family of algorithms which converts *weak learners* to *strong learners*. How does it happen?

**Tags:** machine-learning, gradient-boosting, boosting

> **Comments:**
> **username_1**: No weak learner becomes strong; it is the *ensemble* of the weak learners that turns out to be strong.  

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## 3 answers

### Answer by username_5 on 2020-09-27. Score: 6 | Accepted answer

As @username_1 mentioned in the comment

> No weak learner becomes strong; it is the ensemble of the weak learners that turns out to be strong

Boosting is an ensemble method that integrates multiple models(called as weak learners) to produce a supermodel (Strong learner).

[![Boosting technique is illustrated](https://i.sstatic.net/tdxd7.png)](https://i.sstatic.net/tdxd7.png)

Basically boosting is to train weak learners sequentially, each trying to correct its predecessor. For boosting, we need to specify a weak model (e.g. regression, shallow decision trees, etc.), and then we try to improve each weak learner to learn something from the data.

*AdaBoost* is a boosting algorithm where a decision tree with a single split is used as a weak learner. Also, we have *gradient boosting* and *XG boosting*.

> **Comments:**
> **username_3**: You say "and then improves it.", who improves what?  
> **username_5**: we try to improve each weak learner to learn something from the data  

---

### Answer by username_2 on 2020-09-27. Score: 1

In Boosting, we improve the overall metrics of the model by sequentially building weak models and then building upon the weak metrics of previous models.

We start out by applying basic non-specific algorithms to the problem, which returns some weak prediction functions by taking arbitrary solutions (like sparse weights or assigning equal weights/attention). We improve upon this in the following predictions by adjusting weights to those having a higher error rate. After going through many iterations, we combine it to create a single Strong Prediction Function which has better metrics.

---

Some popular Boosting Algorithms :

* AdaBoost
* Gradient Tree Boosting
* XGBoost

> **Comments:**
> **username_3**: What are "weak prediction functions" and "Strong Prediction Function"? These terms are ambiguous because you did not define what you mean by "weak" and "strong" and you also have not defined "prediction function". Also, what do you mean by "weak metrics"? Do you mean metrics according to which the model performs bad?  

---

### Answer by username_4 on 2020-09-26. Score: 1

You take a bunch of weak learners, each of them trained on a subset of the data.

You then just get all of them to make a prediction, and you learn how much you can trust each one, resulting in a weighted vote or other type of combination of the individual predictions.


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