Answers.Body: <p>Hi and welcome to StackExchange!</p>
  <p>First of all, your dataset is truly, extremely small. Maybe someone can correct me, but I would say 30 points is so small that using RandomForest is not appropriate.</p>
  <p>That aside, overfitting is when your test set performance is worse to training set performance, due to the model fitting itself to noise in the training set. In most cases, you will see SOME degree of this (test set performance worse than training set). However, the question is how much.</p>
  <p>In your case, you have basically a &quot;perfect score&quot; on your training set - this will basically ALWAYS signify some degree of overfitting (in the real world, there's pretty much no way a model can ever &quot;truly&quot; be getting a perfect score unless it's overfitting). This is confirmed by the considerably larger error in the test set.</p>
  <p>The question to you is: Yes, you have overfitting. But is this degree of overfitting acceptable? Is 0.12 error on test set good enough for you? If it is, then you don't need to worry too much. If not, you need to make some changes!</p>
  <p>If that's too much error for you, I would <em><strong>HIGHLY</strong></em> recommend using a simpler method (some form of regression, linear or logistic). Your dataset is too small for such complex algorithms to do well.</p>
  
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Answers.CreationDate: 2022-06-28T03:22:55.833Z
Answers.Text: Thanks Vladimir!
  
  I tried with Linear Regression, Kernel Ridge and Lasso but I didn't get the result I was expecting. I was reading and maybe I could try with a Bayesian Model since it works with small data but I don't know if you could recommend me something else ? tks.
Answers.UserDisplayName: Gaby
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Answers.CreationDate: 2022-06-28T03:25:02.947Z
Answers.Text: @Gaby Can you give me more information about the kind of data you're working with? What variables you're using (how many you have) and how much total data you have? Maybe I could give a recommendation.
Answers.UserDisplayName: Vladimir Belik
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-06-28T03:54:00.907Z
Answers.Text: @Gaby The way you pre-process the data and the features you create are generally far more important than the algorithm you choose. I'm happy to give some suggestions if you give more details, but generally, for such a small amount of data, I would recommend sticking with a simple model (linear reg.) and put your time into pre-processing and feature engineering.
Answers.UserDisplayName: Vladimir Belik
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-06-28T21:56:16.790Z
Answers.Text: I'm trying to predict the temperature in Colombia. For that purpose, Im using the following variables: CO2, population, forest area, agricultural land, agricultural methane emissions, coal consumption, gas consumption, oil consumption, renewables consumption and gdp. The information is available for the period 1990 - 2020 but is annualized. I used Min Max Scaler for standardization and i dont really have null values. Thanks for your help.
Answers.UserDisplayName: Gaby
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Answers.CreationDate: 2022-06-28T23:13:19.643Z
Answers.Text: @Gaby I see. First off, that's a large amount of variables for such a small dataset. Remember that statistics is not magic :) There is no realistic way a model could figure out the relationship between 12 variables given only 30 points to look at. With that in mind, apply a bit of expertise - look at your variables. Do you think that things like GDP, energy consumption, population and even forest area (which probably doesn't change dramatically from year to year) would realistically predict next year's mean temperature? To be honest, personally, I'm very doubtful.
Answers.UserDisplayName: Vladimir Belik
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-06-28T23:15:18.693Z
Answers.Text: @Gaby I know CO2 and methane are greenhouse gasses, but even that I doubt would be any kind of realistic predictor of temperature in the very next year - these processes take a long time to unfold, and they do so in complex ways. If I were you, I would try a basic time series approach. I think realistically, that would be one of the few ways to try to make sense of this data.
Answers.UserDisplayName: Vladimir Belik
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-06-28T00:36:04.297Z
Answers.LastActivityDate: 2022-06-28T00:36:04.297Z
Answers.UserDisplayName: Vladimir Belik
Body: <p>Im creating a Random Forest Regressor Model with a small dataset (30 data points). I tried with other models but RF was the best one, however, after applying GridSearchCv I got that the training set is 0 and the test set is 0.12, and I dont know if that means that my model is overfitted.</p>
  <p>Could anyone please help me with this?</p>
  <p>Tks<a href="https://i.sstatic.net/AZWQd.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/AZWQd.png" alt="RF" /></a></p>
  
Tags: <regression><overfitting><random-forests>
Title: How do I know if my Random Forest Regressor Model is overfitted?
UserDisplayName: Gaby
ThreadText: # How do I know if my Random Forest Regressor Model is overfitted?
  
  (Asked by: username_0 on 2022-06-28)
  
  Im creating a Random Forest Regressor Model with a small dataset (30 data points). I tried with other models but RF was the best one, however, after applying GridSearchCv I got that the training set is 0 and the test set is 0.12, and I dont know if that means that my model is overfitted.
  
  Could anyone please help me with this?
  
  Tks[![RF](https://i.sstatic.net/AZWQd.png)](https://i.sstatic.net/AZWQd.png)
  
  **Tags:** regression, overfitting, random-forests
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-06-28. Score: 1 | Accepted answer
  
  Hi and welcome to StackExchange!
  
  First of all, your dataset is truly, extremely small. Maybe someone can correct me, but I would say 30 points is so small that using RandomForest is not appropriate.
  
  That aside, overfitting is when your test set performance is worse to training set performance, due to the model fitting itself to noise in the training set. In most cases, you will see SOME degree of this (test set performance worse than training set). However, the question is how much.
  
  In your case, you have basically a "perfect score" on your training set - this will basically ALWAYS signify some degree of overfitting (in the real world, there's pretty much no way a model can ever "truly" be getting a perfect score unless it's overfitting). This is confirmed by the considerably larger error in the test set.
  
  The question to you is: Yes, you have overfitting. But is this degree of overfitting acceptable? Is 0.12 error on test set good enough for you? If it is, then you don't need to worry too much. If not, you need to make some changes!
  
  If that's too much error for you, I would ***HIGHLY*** recommend using a simpler method (some form of regression, linear or logistic). Your dataset is too small for such complex algorithms to do well.
  
  > **Comments:**
  > **username_0**: Thanks username_1!  I tried with Linear Regression, Kernel Ridge and Lasso but I didn't get the result I was expecting. I was reading and maybe I could try with a Bayesian Model since it works with small data but I don't know if you could recommend me something else ? tks.  
  > **username_1**: @username_0 Can you give me more information about the kind of data you're working with? What variables you're using (how many you have) and how much total data you have? Maybe I could give a recommendation.  
  > **username_1**: @username_0 The way you pre-process the data and the features you create are generally far more important than the algorithm you choose. I'm happy to give some suggestions if you give more details, but generally, for such a small amount of data, I would recommend sticking with a simple model (linear reg.) and put your time into pre-processing and feature engineering.  
  > **username_0**: I'm trying to predict the temperature in Colombia. For that purpose, Im using the following variables: CO2, population, forest area, agricultural land, agricultural methane emissions, coal consumption, gas consumption, oil consumption, renewables consumption and gdp. The information is available for the period 1990 - 2020 but is annualized. I used Min Max Scaler for standardization and i dont really have null values. Thanks for your help.  
  > **username_1**: @username_0 I see. First off, that's a large amount of variables for such a small dataset. Remember that statistics is not magic :) There is no realistic way a model could figure out the relationship between 12 variables given only 30 points to look at. With that in mind, apply a bit of expertise - look at your variables. Do you think that things like GDP, energy consumption, population and even forest area (which probably doesn't change dramatically from year to year) would realistically predict next year's mean temperature? To be honest, personally, I'm very doubtful.  
  > **username_1**: @username_0 I know CO2 and methane are greenhouse gasses, but even that I doubt would be any kind of realistic predictor of temperature in the very next year - these processes take a long time to unfold, and they do so in complex ways. If I were you, I would try a basic time series approach. I think realistically, that would be one of the few ways to try to make sense of this data.  
  
  ---
  
  