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Does setting a random state in sklearn's RandomForestClassifier bias your model?

Why does this code using random strings print “hello world”?random_state parameter in classification modelsHow to find the accuracy for Survival Analysis using RandomForestSRC package in RHow does one interpret the random forest classifier from sci-kit learn?Why does cross validation RF classification perform worse than without cross validation?Random forest sklearnRandom Forest and ImbalanceUsed SequentialFeatureSelector but does not improve the model accuracyRandom forest: balancing test set?How to find the difference between two Random Forest model instances?

1

I've training a random forest model and am using a consistent random_state value. I'm also getting really good accuracies across my training, test, and validation datasets (all are around ~.98). Though the minority class comprises only ~10% of the dataset.

Here's some code if you're interested:

model = RandomForestClassifier(n_jobs=-1, oob_score=True, random_state=310, n_estimators=300)
model.fit(subset, train.iloc[:,-1])

Given the good accuracy scores across training, validation and testing datsets, does random_state affect the generalization of my model?

random scikit-learn random-forest random-seed

share|improve this question

edited Mar 9 at 8:59

AI_Learning

4,15521035

asked Mar 8 at 20:58

Joe BJoe B

16812

add a comment | 

1

I've training a random forest model and am using a consistent random_state value. I'm also getting really good accuracies across my training, test, and validation datasets (all are around ~.98). Though the minority class comprises only ~10% of the dataset.

Here's some code if you're interested:

model = RandomForestClassifier(n_jobs=-1, oob_score=True, random_state=310, n_estimators=300)
model.fit(subset, train.iloc[:,-1])

Given the good accuracy scores across training, validation and testing datsets, does random_state affect the generalization of my model?

random scikit-learn random-forest random-seed

share|improve this question

edited Mar 9 at 8:59

AI_Learning

4,15521035

asked Mar 8 at 20:58

Joe BJoe B

16812

add a comment | 

I've training a random forest model and am using a consistent random_state value. I'm also getting really good accuracies across my training, test, and validation datasets (all are around ~.98). Though the minority class comprises only ~10% of the dataset.

Here's some code if you're interested:

model = RandomForestClassifier(n_jobs=-1, oob_score=True, random_state=310, n_estimators=300)
model.fit(subset, train.iloc[:,-1])

Given the good accuracy scores across training, validation and testing datsets, does random_state affect the generalization of my model?

random scikit-learn random-forest random-seed

share|improve this question

edited Mar 9 at 8:59

AI_Learning

4,15521035

asked Mar 8 at 20:58

Joe BJoe B

16812

model = RandomForestClassifier(n_jobs=-1, oob_score=True, random_state=310, n_estimators=300)
model.fit(subset, train.iloc[:,-1])

share|improve this question

edited Mar 9 at 8:59

AI_Learning

4,15521035

asked Mar 8 at 20:58

Joe BJoe B

16812

share|improve this question

edited Mar 9 at 8:59

AI_Learning

4,15521035

asked Mar 8 at 20:58

Joe BJoe B

16812

edited Mar 9 at 8:59

AI_Learning

4,15521035

edited Mar 9 at 8:59

AI_Learning

4,15521035

asked Mar 8 at 20:58

Joe BJoe B

16812

asked Mar 8 at 20:58

Joe BJoe B

16812

2 Answers
2

active

oldest

votes

1

random_state does not affect the generalization of the model. In fact, it is the best practice to have same value for random_state when you tune your hyper parameters such as n_estimators, depth, etc. This will ensure that your performance is not affected by the random initial state.

Also, Accuracy is not the recommended metrics to measure the performance of the model, when you have such as unbalanced dataset.

Area under the ROC or PR curve could be one of the few best things you can use but there are a lot of metrics available. See here

share|improve this answer

answered Mar 9 at 8:57

AI_LearningAI_Learning

4,15521035

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Thanks! just what I was looking for. re accuracy - I'm actually using a confusion matrix to measure for internal metrics, but that is a good point.
  
  – Joe B
  Mar 9 at 17:26
  

  
  
  
  

add a comment | 

1

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

In general random_state is be used to set the internal parameters initially, so you can repeat the training deterministically. Now you can change other hyperparameters (e.g. number of trees) to compare the results.

A disadvantage could be that you don't find the global optimum. But your results sound really good with an accuracy of 0.98.

share|improve this answer

answered Mar 8 at 21:19

Darius M.Darius M.

3,50012036

add a comment | 

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1

random_state does not affect the generalization of the model. In fact, it is the best practice to have same value for random_state when you tune your hyper parameters such as n_estimators, depth, etc. This will ensure that your performance is not affected by the random initial state.

Also, Accuracy is not the recommended metrics to measure the performance of the model, when you have such as unbalanced dataset.

Area under the ROC or PR curve could be one of the few best things you can use but there are a lot of metrics available. See here

share|improve this answer

answered Mar 9 at 8:57

AI_LearningAI_Learning

4,15521035

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Thanks! just what I was looking for. re accuracy - I'm actually using a confusion matrix to measure for internal metrics, but that is a good point.
  
  – Joe B
  Mar 9 at 17:26
  

  
  
  
  

add a comment | 

1

random_state does not affect the generalization of the model. In fact, it is the best practice to have same value for random_state when you tune your hyper parameters such as n_estimators, depth, etc. This will ensure that your performance is not affected by the random initial state.

Also, Accuracy is not the recommended metrics to measure the performance of the model, when you have such as unbalanced dataset.

Area under the ROC or PR curve could be one of the few best things you can use but there are a lot of metrics available. See here

share|improve this answer

answered Mar 9 at 8:57

AI_LearningAI_Learning

4,15521035

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Thanks! just what I was looking for. re accuracy - I'm actually using a confusion matrix to measure for internal metrics, but that is a good point.
  
  – Joe B
  Mar 9 at 17:26
  

  
  
  
  

add a comment | 

random_state does not affect the generalization of the model. In fact, it is the best practice to have same value for random_state when you tune your hyper parameters such as n_estimators, depth, etc. This will ensure that your performance is not affected by the random initial state.

Also, Accuracy is not the recommended metrics to measure the performance of the model, when you have such as unbalanced dataset.

Area under the ROC or PR curve could be one of the few best things you can use but there are a lot of metrics available. See here

share|improve this answer

answered Mar 9 at 8:57

AI_LearningAI_Learning

4,15521035

share|improve this answer

answered Mar 9 at 8:57

AI_LearningAI_Learning

4,15521035

answered Mar 9 at 8:57

AI_LearningAI_Learning

4,15521035

answered Mar 9 at 8:57

AI_LearningAI_Learning

4,15521035

1

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

In general random_state is be used to set the internal parameters initially, so you can repeat the training deterministically. Now you can change other hyperparameters (e.g. number of trees) to compare the results.

A disadvantage could be that you don't find the global optimum. But your results sound really good with an accuracy of 0.98.

share|improve this answer

answered Mar 8 at 21:19

Darius M.Darius M.

3,50012036

add a comment | 

1

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

In general random_state is be used to set the internal parameters initially, so you can repeat the training deterministically. Now you can change other hyperparameters (e.g. number of trees) to compare the results.

A disadvantage could be that you don't find the global optimum. But your results sound really good with an accuracy of 0.98.

share|improve this answer

answered Mar 8 at 21:19

Darius M.Darius M.

3,50012036

add a comment | 

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

In general random_state is be used to set the internal parameters initially, so you can repeat the training deterministically. Now you can change other hyperparameters (e.g. number of trees) to compare the results.

A disadvantage could be that you don't find the global optimum. But your results sound really good with an accuracy of 0.98.

share|improve this answer

answered Mar 8 at 21:19

Darius M.Darius M.

3,50012036

share|improve this answer

answered Mar 8 at 21:19

Darius M.Darius M.

3,50012036

answered Mar 8 at 21:19

Darius M.Darius M.

3,50012036

answered Mar 8 at 21:19

Darius M.Darius M.

3,50012036