Data Training Set

Curated data set used for 🏋️ Model Training and Tuning

Notes

Data training sets are collections of data prepared for machine learning model development. They usually include labeled examples for supervised learning tasks and can be created using data preprocessing techniques like normalization, feature scaling, and handling missing values.

TakeAways

• 📌 Create high-quality datasets to improve model performance.
  • ⚒️ Data Collection: Gather data from reliable sources relevant to the ML task.
  • 🔎 Data Preprocessing: Cleanse data, handle missing values, and normalize features.
• 💡 Datasets should be representative of the target population to avoid bias in models.

Process

• Define the problem and required data.
• Collect data from suitable sources like CSV files, APIs, or databases.
• Cleanse data by handling missing values and removing duplicates.
• Preprocess data by normalizing features, encoding categorical variables, etc.
• Split dataset into training and validation sets.

Thoughts

• 🔎 Data Privacy: Ensure datasets comply with privacy regulations to protect sensitive information.
• 📉 Evaluation Metrics: Choose appropriate evaluation metrics based on the ML task (e.g., accuracy, precision, recall, F1-score).

Related

1. Data Split
2. Data Testing datasets