Added best_random_state_in_random_forest.py file an algorithm to find best random state of random forest classifier #12277
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The code is designed to identify the optimal random state for a Random Forest Classifier to maximize its prediction accuracy on a given dataset. It uses the heart.csv dataset. The logic is encapsulated within the function find_best_random_state, which accepts a DataFrame and the name of the target column as arguments.
Create best_random_state_in_random_forest.py
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| warnings.filterwarnings('ignore') | ||
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| def find_best_random_state(data: pd.DataFrame, target_column: str, iterations: int = 200) -> int: |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/best_random_state_in_random_forest.py, please provide doctest for the function find_best_random_state
| target = data[target_column] | ||
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| # Split dataset into train and test sets | ||
| X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size=0.20, random_state=0) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_train
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_test
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| # Scale features | ||
| scaler = StandardScaler() | ||
| X_train_scaled = scaler.fit_transform(X_train) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_train_scaled
| # Scale features | ||
| scaler = StandardScaler() | ||
| X_train_scaled = scaler.fit_transform(X_train) | ||
| X_test_scaled = scaler.transform(X_test) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_test_scaled
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Closing require_tests PRs to prepare for Hacktoberfest |
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Description of Change
The code is designed to identify the optimal random state for a Random Forest Classifier to maximize its prediction accuracy on a given dataset. It uses the heart.csv dataset. The logic is encapsulated within the function
find_best_random_state, which accepts a DataFrame and the name of the target column as arguments.Describe your change:
Added an algorithm to find the best optimal random state of Random Forest Classifier.
Checklist: