Documentation Index
Fetch the complete documentation index at: https://sourcetable.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
Feature engineering turns raw data into inputs that improve model performance. Sourcetable automates common transformations through the AI assistant.
Missing value handling
"Handle missing values in the dataset — use median for numeric and mode for categorical"
| Strategy | When to use |
|---|
| Mean/median imputation | Numeric columns, few missing values |
| Mode imputation | Categorical columns |
| Forward/backward fill | Time series data |
| KNN imputation | Values depend on other features |
| Drop rows | Small percentage missing, large dataset |
| Indicator column | Missingness itself is informative |
Encoding categorical variables
"Encode all categorical columns for the ML model"
| Method | When to use |
|---|
| One-hot encoding | Nominal categories (color, city) with low cardinality |
| Label encoding | Ordinal categories (low/medium/high) |
| Target encoding | High-cardinality categories (zip code, product ID) |
| Binary encoding | High-cardinality with many categories |
| Frequency encoding | When category frequency matters |
Scaling and normalization
"Normalize all numeric features to 0-1 range"
| Method | When to use |
|---|
| StandardScaler (Z-score) | Most ML algorithms, normally distributed data |
| MinMaxScaler (0-1) | Neural networks, distance-based algorithms |
| RobustScaler | Data with outliers |
| Log transform | Right-skewed distributions (income, prices) |
| Box-Cox transform | Make data more normal, various skew levels |
Feature creation
Date/time features
"Extract year, month, day of week, and hour from the timestamp column"
Aggregation features
"Create customer-level features: total purchases, average order value, days since last order"
Interaction features
"Create interaction features between price and quantity"
Text features
"Extract features from the product description column"
Feature selection
Importance-based
"Rank features by importance using a random forest"
Variance Inflation Factor (VIF)
"Check for multicollinearity and drop highly correlated features"
Recursive Feature Elimination (RFE)
"Use RFE to find the optimal set of features for the model"
