Hyperparameter tuning for Random Forest model

• Day: 2023-01-12
• Time: 19:00 to 19:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Machine Learning, Random Forest, Pandas, Model Evaluation

Description

Session Goal: The session aimed to optimize the performance of a Random Forest model by iterating over different hyperparameters and improving data manipulation techniques in Python.

Key Activities:

• Implemented Python code to evaluate a Random Forest model’s performance by iterating over different values of the max_depth hyperparameter and calculating the mean absolute error (MAE) for both training and test sets.
• Demonstrated the use of pd.concat() for more efficient DataFrame concatenation in Pandas, as opposed to the append() method.
• Addressed a feature name warning in RandomForestClassifier by ensuring correct feature names during model fitting.
• Explained the use of the index parameter in the pd.[[DataFrame]]() constructor and provided examples for combining DataFrames.
• Aggregated model performance metrics by grouping a DataFrame by model parameters and calculating training and testing MAE.
• Calculated quantiles for model evaluation metrics using the quantile() method in Pandas.

Achievements:

• Successfully iterated over hyperparameters and evaluated model performance metrics.
• Improved data manipulation techniques in Python, particularly with Pandas.
• Resolved warnings related to feature names in RandomForestClassifier.

Pending Tasks:

• Further exploration of additional hyperparameters for model optimization.
• Review and validation of the quantile calculation method to ensure no overwriting of keys in aggregation.

Evidence

• source_file=2023-01-12.sessions.jsonl, line_number=0, event_count=0, session_id=5a87d9fad3e9f92843d28129d749dcc5a6a6a57c25d25b3f1e91c94584486ce0
• event_ids: []