blocks|key|2019727|text|您可以使用h2o.grid()进行网格搜索。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2019728|#+specify+your+hyper+parameters
hyper_params+=+list(+ntrees+=+c(100,200,300,400),+learn_rate+=++c(1,0.5,0.1)+)

#+then+build+your+grid
grid+<-+h2o.grid(
##+hyper+parameters
hyper_params+=+hyper_params,

##+which+algorithm+to+run
algorithm+=+"gbm",

##+identifier+for+the+grid,+to+later+retrieve+it
grid_id+=+"my_grid",+

##+standard+model+parameters
x+=+features,+
y+=+label,+
training_frame+=+train,+
validation_frame+=+valid,

##+set+a+seed+for+reproducibility
seed+=+1234)|code-block|syntax|javascript|2019729|您可以阅读更多关于h2o.grid()如何在R文档package.pdf中工作的内容。|2019730|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.h2o.ai/h2o/latest-stable/h2o-r/h2o_package.pdf^0|5|A|0|0|P|B|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|M|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

you can use <code>h2o.grid()</code> to do your grid search

<pre><code># specify your hyper parameters
hyper_params = list( ntrees = c(100,200,300,400), learn_rate = c(1,0.5,0.1) )

# then build your grid
grid &lt;- h2o.grid(
## hyper parameters
hyper_params = hyper_params,

## which algorithm to run
algorithm = "gbm",

## identifier for the grid, to later retrieve it
grid_id = "my_grid", 

## standard model parameters
x = features, 
y = label, 
training_frame = train, 
validation_frame = valid,

## set a seed for reproducibility
seed = 1234)
</code></pre>

you can read more about how h2o.grid() works in the R documentation <a href="http://docs.h2o.ai/h2o/latest-stable/h2o-r/h2o_package.pdf" rel="nofollow">http://docs.h2o.ai/h2o/latest-stable/h2o-r/h2o_package.pdf</a>

blocks|key|3923061|text|使用网格的Lauren's+answer是这里最好的。我将很快地指出，您所编写的是一种有用的方法，当网格不做您需要的事情时，您可以依靠它。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3923062|您的示例没有包含任何数据(请参阅https://stackoverflow.com/help/mcve)，因此我无法运行它，但是我纠正了我注意到的两个语法问题(R的for-in循环直接给出了值，而不是索引，第二个for循环的圆括号)：|3923063|ntrees+<-+c(100,200,300,400)
learn_rate+<-+c(1,0.5,0.1)
for+(n+in+ntrees){
++for+(l+in+learn_rate){
++++gbm_model+<-+h2o.gbm(
++++++features,+label,+training_frame+=+train,+validation_frame+=+valid,
++++++ntrees+=+n,max_depth+=+5,learn_rate+=+l
++++++)
++++print(c(n,l,h2o.mse(h2o.performance(gbm_model,+valid+=+TRUE))))
++}
}|code-block|syntax|javascript|3923064|在何时使用嵌套循环的一个例子是，当您想要跳过某些组合时。例如，您可能决定只测试100的ntree，其学习速率为0.1，然后如下所示：|3923065|ntrees+<-+c(100,200,300,400)
learn_rate+<-+c(1,0.5,0.1)
for+(n+in+ntrees){
++for+(l+in+learn_rate){
++++if(l+==+0.1+&&+n+>+100)next++#Skip+when+n+is+200,300,400
++++gbm_model+<-+h2o.gbm(
++++++features,+label,+training_frame+=+train,+validation_frame+=+valid,
++++++ntrees+=+n,max_depth+=+5,learn_rate+=+l
++++++)
++++print(c(n,l,h2o.mse(h2o.performance(gbm_model,+valid+=+TRUE))))
++}
}|3923066|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/40009966/841830|1|https://stackoverflow.com/help/mcve^0|5|F|0|0|G|Z|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|D|3|E|5|6|7|12|8|@]|9|@$A|13|B|14|1|15]]|C|$]]|$1|F|3|G|5|H|7|16|8|@]|9|@]|C|$I|J]]|$1|K|3|L|5|6|7|17|8|@]|9|@]|C|$]]|$1|M|3|N|5|H|7|18|8|@]|9|@]|C|$I|J]]|$1|O|3|-4|5|6|7|19|8|@]|9|@]|C|$]]]|P|$Q|$5|R|S|T|C|$U|V]]|W|$5|R|S|T|C|$U|X]]]]

<a href="https://stackoverflow.com/a/40009966/841830">Lauren's answer</a>, to use grids, is the best one here. I'll just quickly point out that what you have written is a usable approach, and one you can fall back on when grids don't do something you need.

Your example didn't include any data (see <a href="https://stackoverflow.com/help/mcve">https://stackoverflow.com/help/mcve</a>) so I couldn't run it, but I corrected the couple of syntax issues I noticed (R's for-in loop directly gives you the value, not the index, and parentheses around the 2nd for loop):

<pre><code>ntrees &lt;- c(100,200,300,400)
learn_rate &lt;- c(1,0.5,0.1)
for (n in ntrees){
 for (l in learn_rate){
 gbm_model &lt;- h2o.gbm(
 features, label, training_frame = train, validation_frame = valid,
 ntrees = n,max_depth = 5,learn_rate = l
 )
 print(c(n,l,h2o.mse(h2o.performance(gbm_model, valid = TRUE))))
 }
}
</code></pre>

An example of when you'd use nested loops, like this, is when you want to skip certain combinations. E.g. You might decide to only test ntrees of 100 with learn rate of 0.1, which would then look like this:

<pre><code>ntrees &lt;- c(100,200,300,400)
learn_rate &lt;- c(1,0.5,0.1)
for (n in ntrees){
 for (l in learn_rate){
 if(l == 0.1 &amp;&amp; n &gt; 100)next #Skip when n is 200,300,400
 gbm_model &lt;- h2o.gbm(
 features, label, training_frame = train, validation_frame = valid,
 ntrees = n,max_depth = 5,learn_rate = l
 )
 print(c(n,l,h2o.mse(h2o.performance(gbm_model, valid = TRUE))))
 }
}
</code></pre>

I need to perform parameter optimization on a gbm model on RH2o. I am relatively new to H2o and I think I need to convert ntrees and learn_rate(below) into a H2o vector before performing the below.
How do I perform this operation?
Thanks!

<pre><code>ntrees &lt;- c(100,200,300,400)
learn_rate &lt;- c(1,0.5,0.1)
for (i in ntrees){
 for j in learn_rate{
 n = ntrees[i]
 l= learn_rate[j]
 gbm_model &lt;- h2o.gbm(features, label, training_frame = train, validation_frame = valid, ntrees=ntrees[[i]],max_depth = 5,learn_rate=learn_rate[j])
 print(c(ntrees[i],learn_rate[j],h2o.mse(h2o.performance(gbm_model, valid = TRUE))))

 }
}
</code></pre>

Parameter optimization in R and H2O

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我需要对RH2o上的gbm模型进行参数优化。我对H2o比较陌生，我认为在执行下面的操作之前，我需要将ntree和learn_rate(下面)转换为H2o向量。我该怎么做这个手术？谢谢!ntrees <- c(100,200,300,400)learn_rate <- c(1,0.5,0.1)for (i in ntre...

问R和H2O中的参数优化
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