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

what is random state in sklearn train_test_split
sklearn random seed
what is random state in logistic regression
random state in decision tree
random state in machine learning
the purpose of setting the random_state parameter in train_test_split is
train_test_split sklearn
how to split data into training and testing in python

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_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

How to set the global random_state in Scikit Learn, What to do if you keep forgetting to set the random_state? Scikit Learn does not have its own global random state but uses the numpy� sklearn.model_selection.KFold class sklearn.model_selection.KFold(n_splits=’warn’, shuffle=False, random_state=None) [source] K-Folds cross-validator. Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

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.

sklearn.model_selection.train_test_split — scikit-learn 0.23.1 , If train_size is also None, it will be set to 0.25. train_sizefloat or int, default= random_stateint or RandomState instance, default=None. Controls the shuffling� '''Regarding the random state, it is used in many randomized algorithms in sklearn to determine the random seed passed to the pseudo-random number generator. Therefore, it does not govern any aspect of the algorithm's behavior.

The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. Consequently, practical decision-tree learning algorithms are based on heuristic algorithms such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot guarantee to return the globally optimal decision tree. This can be mitigated by training multiple trees in an ensemble learner, where the features and samples are randomly sampled with replacement.

random_state is for random selections of sub-features, smaller than the total features, and sub-samples. This parameter controls random choices.

random_state in Machine Learning, Also what does the value 0, 1 and other values of random_state signifies. @ samratp Could you please let me know how to set the randomstate globally in my project or script?. So that from sklearn.ensemble import RandomForestClassifier I got the same issue when using StratifiedKFold setting the random_State to be None. The splits each time is the same. even though I passed different seed generated by np.random.default_rng, it still does not work `rg = np.random.default_rng() seed = rg.integers(1000) skf = StratifiedKFold(n_splits=5, random_state=seed) skf_accuracy = [] skf_f1

Python random state in splitting dataset, can anyone tell me why we set random state to zero in splitting train and test set. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=� You can also set the random_state to 0 as shown below: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0) Note: Sklearn train_test_split function ignores the original sequence of numbers. After a split, they can be presented in a different order.

Is random state a parameter to tune?, As opposed to regular parameters, hyperparameters can not be Regarding the random state, it is used in many randomized algorithms in sklearn to What does the random_state effect? training and validation set splitting,� The random_state parameter allows controlling these random choices. The interface documentation specifically states: 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.

Random state (Pseudo-random number) in Scikit learn, Python random state in splitting dataset, If random_state is None or np.random, then a randomly-initialized RandomState Setting random_state a fixed value will � sklearn.model_selection.KFold¶ class sklearn.model_selection.KFold (n_splits=5, *, shuffle=False, random_state=None) [source] ¶ K-Folds cross-validator. Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

Comments
  • 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.