Feature Engineering та інженерія ознак

🎓 Feature Creation

Domain Knowledge

Concept: Utilizing domain knowledge to create features.

Examples: Age groups, ratios, interactions.

Importance: Critical for success.

Feature Interactions

Concept: Combinations of features (multiplication, division).

Examples: Price/Area, Age*Income.

Application: For nonlinear relationships.

Temporal Features

Concept: Features from time-based data.

Examples: Day of week, hour, season.

Application: Time series, temporal patterns.

🔧 Feature Transformation

Scaling & Normalization

StandardScaler: Mean=0, std=1.

MinMaxScaler: Range [0,1].

RobustScaler: Median, IQR-based.

Encoding

One-Hot: For categorical without order.

Label Encoding: For categorical with order.

Target Encoding: Mean target per category.

Polynomial Features

Concept: Creating polynomial combinations.

Application: For nonlinear relationships.

Caution: Can lead to overfitting.

📚 Practical Examples

Example 1: Creating interaction features

Analysis: Identify important features.

Interactions: Create multiplication, division features.

Evaluation: Verify the importance of new features.

Example 2: Feature selection

Filter: Use correlation for filtering.

Wrapper: Recursive feature elimination.

Evaluation: Compare performance with and without selection.

Try it live

Everything above runs in your browser — open Feature Engineering Transform Graph and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.