📅 2023-04-19 — Session: Implemented ensemble classifiers for categorical data
🕒 19:50–20:00
🏷️ Labels: Ensemble Learning, Random Forest, Data Preprocessing, Python, Machine Learning
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The goal of this session was to explore and implement ensemble classifiers, specifically focusing on handling categorical features in classification tasks using Random Forest and Gradient Boosting.
Key Activities:
- Discussed the use of ensemble classifiers like Random Forest for categorical features.
- Provided Python code examples for training and evaluating Random Forest classifiers using accuracy scores.
- Demonstrated filtering of data to include only labeled data and training a Random Forest classifier to obtain probability scores.
- Addressed handling of NaN values in input data for classifiers, with methods including removal, imputation, and using XGBoost.
- Resolved an index error in NumPy arrays by converting them to pandas DataFrames, enabling proper indexing.
- Provided guidance on converting numpy ndarrays to pandas DataFrames to resolve AttributeErrors.
Achievements:
- Successfully implemented and demonstrated the use of Random Forest and Gradient Boosting for categorical data classification.
- Resolved common data preprocessing issues such as handling NaN values and index errors in NumPy arrays.
Pending Tasks:
- Further exploration of ensemble methods for different types of data.
- Optimization of classifier performance through hyperparameter tuning.