Answers.Body: <p>Yes, you can. Let's say you have 5 classes named <code>a,b,c,d,e</code>. You fit your data into a SVM Classifier and a Random Forest Classifier. Assume that, SVM classified <code>&quot;a&quot;</code> and <code>&quot;b&quot;</code> class well and RFC classified <code>&quot;c&quot;,&quot;d&quot;,&quot;e&quot;</code> well. So, ensembling these two models is going to increase accuracy  dramatically. Ensemble learning is really good when a model generating many <code>&quot;False-Positive&quot;</code> and <code>&quot;False-Negative&quot;</code>. You can also use weighted ensemble learning methods. You can set greater weights for reliable models &amp; lower weights for untrustable models.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-04-01T14:06:20.940Z
Answers.LastActivityDate: 2021-04-01T14:06:20.940Z
Answers.UserDisplayName: dasmehdix
Body: <p>I want to predict using the same model as multivariate time series data in a time series prediction problem.</p>
  <p>Example:</p>
  <pre><code>pa = model predict result(a)
  pb = model predict result(b)
  pc = model predict result(c)
  ...
  model ensemble([pa, pb, pc,...]) -&gt; predict(y)
  </code></pre>
  <p>Can I expect a better performance of our model by using a model ensemble with more kinds of time series data here?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-01T14:15:17.987Z
Comments.Text: Do you mean that would be a single model, but you have multiple timeseries with same target and you plan to ensemble prediction for these timeseries?
Comments.UserDisplayName: Kirill Fedyanin
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-01T14:45:08.330Z
Comments.Text: Yes. I want to make predictions with multiple time series data of different kinds with the same model.
Comments.UserDisplayName: KYH
Tags: <machine-learning><ensemble-learning>
Title: In ensemble learning, does accuracy increase depending on the number of models you want to combine?
UserDisplayName: KYH
ThreadText: # In ensemble learning, does accuracy increase depending on the number of models you want to combine?
  
  (Asked by: username_0 on 2021-04-01)
  
  I want to predict using the same model as multivariate time series data in a time series prediction problem.
  
  Example:
  
  ```
  pa = model predict result(a)
  pb = model predict result(b)
  pc = model predict result(c)
  ...
  model ensemble([pa, pb, pc,...]) -> predict(y)
  
  ```
  
  Can I expect a better performance of our model by using a model ensemble with more kinds of time series data here?
  
  **Tags:** machine-learning, ensemble-learning
  
  > **Comments:**
  > **username_1**: Do you mean that would be a single model, but you have multiple timeseries with same target and you plan to ensemble prediction for these timeseries?  
  > **username_0**: Yes. I want to make predictions with multiple time series data of different kinds with the same model.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2021-04-01. Score: 0 | Accepted answer
  
  Yes, you can. Let's say you have 5 classes named `a,b,c,d,e`. You fit your data into a SVM Classifier and a Random Forest Classifier. Assume that, SVM classified `"a"` and `"b"` class well and RFC classified `"c","d","e"` well. So, ensembling these two models is going to increase accuracy dramatically. Ensemble learning is really good when a model generating many `"False-Positive"` and `"False-Negative"`. You can also use weighted ensemble learning methods. You can set greater weights for reliable models & lower weights for untrustable models.
  
  
  ---
  
  