Optimized Random Forest Model Hyperparameters

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📅 2023-01-12 — Session: Optimized Random Forest Model Hyperparameters

🕒 19:00–19:30
🏷️ Labels: Random Forest, Hyperparameter Tuning, Pandas, Model Evaluation, Data Analysis
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective of this session was to optimize the hyperparameters of a Random Forest model, specifically focusing on the max_depth parameter, to improve model evaluation metrics.

Key Activities

  • Hyperparameter Iteration: Iterated over different values of the max_depth hyperparameter in a Random Forest model, calculating the mean absolute error (MAE) for both training and test sets. Results were stored in a pandas DataFrame for further analysis.
  • DataFrame Optimization: Enhanced DataFrame concatenation techniques in pandas by using pd.concat() for more efficient data manipulation.
  • Warning Resolution: Addressed feature name warnings in RandomForestClassifier by ensuring correct feature names were used during model fitting.
  • Performance Metrics Aggregation: Aggregated model performance metrics by grouping DataFrame data by model parameters and calculating statistics like mean absolute errors.
  • Quantile Calculation: Calculated quantiles for model evaluation metrics to provide insights into the distribution of training and testing MAE.

Achievements

  • Successfully iterated over hyperparameters and stored results effectively.
  • Improved DataFrame operations in pandas, leading to more efficient data processing.
  • Resolved warnings related to feature names in RandomForestClassifier.
  • Aggregated and analyzed model performance metrics to better understand model behavior.

Pending Tasks

  • Further exploration of other hyperparameters for optimization.
  • Detailed analysis of quantile results to inform future model adjustments.

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