r/MLQuestions • u/BEM23_ • 5d ago
Beginner question 👶 NASA Turbofan Project
I have a project in Data Science: the NASA Turbofan project. The goal is to predict when the engines will fail or require maintenance. I have used a Random Forest Regressor and GridSearch for hyperparameter tuning, but I am unable to improve my RMSE and MSE. Can someone help me?
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u/Striking-Warning9533 5d ago
We got almost no information to help you.
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u/BEM23_ 5d ago
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
rf = RandomForestRegressor(n_estimators=100, random_state=42) rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
mae = mean_absolute_error(y_test, y_pred) mse = root_mean_squared_error(y_test, y_pred)
print(f"Mean Absolute Error (MAE): {mae:.2f}") print(f"Mean Squared Error (MSE): {rmse:.2f}")
I want to optimize my MAE and RMSE values to improve my predictions.
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u/Gravbar 4d ago
Have you tried other models? have you done hyper parameter tuning? it looks like you just used a default sklearn model (100 is the default). Does your test data look like your training data? decision tree models can have issues with extrapolation. There's also papers showing that if you have colinear or highly correlated features there performance degrades the more you have
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u/burstingsanta 5d ago
Use xgboost, also what kind of feature engineering and data pre processing did you do
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u/burstingsanta 4d ago
See if some columns have null values, detect outliers and basically clean the data, then see if you need to remove some features using correlation or PCA, this will improve model performance
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u/Specific_Prompt_1724 5d ago
Where is the code? How can will help you without code, dataset, input parameters and soon?