Unexpected behavior when tuning hyperparameters with scikit-learn's GridSearchCV
I'm stuck trying to I'm relatively new to this, so bear with me..... I'm using `GridSearchCV` from `scikit-learn` (version 0.24.2) to tune the hyperparameters of my `RandomForestClassifier`. However, I notice that the performance metrics are inconsistent across different runs, even though I'm setting a fixed random state. I set up the grid search like this: ```python from sklearn.model_selection import GridSearchCV from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # Load dataset X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Initialize model rf = RandomForestClassifier(random_state=42) param_grid = { 'n_estimators': [50, 100], 'max_depth': [None, 10, 20], } grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, scoring='accuracy') grid_search.fit(X_train, y_train) ``` I expect the accuracy scores to be similar across runs since I am using `random_state=42` for both the classifier and train-test split. However, when I run this multiple times, I see accuracy scores varying from 0.9 to 1.0. Is it possible that the random features and bootstrap sampling in `RandomForestClassifier` are causing this variability? Should I be setting a specific random state for the grid search itself? I've also tried using a `StratifiedKFold` for cross-validation, but the results remain inconsistent. Am I missing something here in terms of best practices for hyperparameter tuning, or is there an inherent randomness in the process I should be aware of? Any insights would be appreciated! For context: I'm using Python on macOS. This is part of a larger service I'm building.