Data Transformations and Feature Engineering
Last updated July 29, 2024
Raw data often requires transformation and engineering before it can be effectively used for machine learning. This process of manipulating and creating new features can significantly improve model performance and accuracy. In Mostly AI, you have powerful tools to perform these critical data preparation steps.
Data Transformations
- Data Scaling: Transforming data to fall within a specific range (like 0 to 1) is crucial for some algorithms that are sensitive to scale. Common scaling methods include:
- Standardization: Center the data around zero and set a unit variance.
- Normalization: Scale data to a range between 0 and 1.
- Data Encoding: Converting categorical variables (textual values) into numerical representations that algorithms can understand. Techniques include:
- One-Hot Encoding: Creating a binary column for each unique category.
- Label Encoding: Assigning a numerical value to each category.
- Feature Aggregation: Combining multiple features into a single new feature. This can be done by:
- Summing or Averaging: Calculating the sum or average of multiple features.
- Creating Ratios: Dividing one feature by another to represent a proportional relationship.
- Data Imputation: Handling missing values by replacing them with appropriate estimates. Techniques include:
- Mean/Median Imputation: Replacing missing values with the mean or median of the feature.
- K-Nearest Neighbors Imputation: Using values from nearby data points to fill missing values.
Feature Engineering
- Creating Interaction Terms: Multiplying two or more features to capture non-linear relationships between them.
- Generating Polynomial Features: Creating new features by raising existing features to different powers.
- Extracting Textual Features: Applying techniques like TF-IDF or word embeddings to extract meaningful features from textual data.
- Creating Time-Based Features: Deriving features from dates and timestamps, like day of the week, month, or time elapsed.
By intelligently transforming and engineering features, you can improve the quality of your data, enhance its relevance to your model, and achieve better prediction accuracy.
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