👀 Views: 58 💬 Answers: 1 📅 Created: 2025-06-10

scikit-learn machine-learning random-forest Python

I'm maintaining legacy code that I'm currently working on a regression question using scikit-learn version 1.0. While fitting a `RandomForestRegressor`, I noticed that my predictions contain NaN values, which is quite perplexing. My training data is preprocessed using StandardScaler, and I ensured that there are no NaN values in the input features. However, when I run the fitting process, I occasionally see warnings related to NaN values in the target variable, even though I’ve checked it thoroughly. Here’s the code snippet I'm using: ```python import pandas as pd from sklearn.ensemble import RandomForestRegressor from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split # Sample data creation X = pd.DataFrame({ 'feature1': [1, 2, 3, 4, 5], 'feature2': [5, 4, 3, 2, 1] }) y = pd.Series([1, 2, 3, 4, np.nan]) # Notice the NaN in target # Splitting data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Scaling features scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # Fitting the model model = RandomForestRegressor(random_state=42) model.fit(X_train_scaled, y_train) ``` Despite my efforts to deal with the NaN values in the target variable by dropping them before fitting, the model occasionally outputs NaN predictions. I've tried several approaches, such as using `SimpleImputer` to fill missing values or adjusting the random forest parameters, but the scenario continues. Could anyone provide guidance on how to resolve this scenario, or suggest any best practices for handling missing values in both features and targets when using RandomForest in scikit-learn? Thanks in advance! This issue appeared after updating to Python latest. I'd be grateful for any help.