Creating Interaction Features
Interaction features are new variables formed by combining two or more existing features, often through mathematical operations such as multiplication, division, or addition, to reflect how these variables jointly influence the target.
Creating interaction features allows you to capture complex relationships between variables in the Titanic dataset, such as Age, Fare, Pclass, and Sex. The influence of one variable on survival can depend on another variable's value. For example, the effect of passenger class on survival may differ for males and females, or younger passengers might benefit more from higher fares. By combining features like Age * Fare or Pclass * Sex_encoded, you enable your model to learn these nuanced patterns, improving its ability to predict who survived based on how variables interact.
1234567891011121314151617181920import pandas as pd # Sample Titanic-like dataset data = { "Age": [22, 38, 26, 35, 28], "Fare": [7.25, 71.28, 7.92, 53.10, 8.05], "Pclass": [3, 1, 3, 1, 3], "Sex": ["male", "female", "female", "female", "male"], "Survived": [0, 1, 1, 1, 0] } df = pd.DataFrame(data) # Encode 'Sex' as a numeric feature df["Sex_encoded"] = df["Sex"].map({"male": 0, "female": 1}) # Create interaction features df["Age_Fare_product"] = df["Age"] * df["Fare"] df["Pclass_Sex_interaction"] = df["Pclass"] * df["Sex_encoded"] print(df[["Age", "Fare", "Pclass", "Sex", "Age_Fare_product", "Pclass_Sex_interaction", "Survived"]])
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Interaction features are new variables formed by combining two or more existing features, often through mathematical operations such as multiplication, division, or addition, to reflect how these variables jointly influence the target.
Creating interaction features allows you to capture complex relationships between variables in the Titanic dataset, such as Age, Fare, Pclass, and Sex. The influence of one variable on survival can depend on another variable's value. For example, the effect of passenger class on survival may differ for males and females, or younger passengers might benefit more from higher fares. By combining features like Age * Fare or Pclass * Sex_encoded, you enable your model to learn these nuanced patterns, improving its ability to predict who survived based on how variables interact.
1234567891011121314151617181920import pandas as pd # Sample Titanic-like dataset data = { "Age": [22, 38, 26, 35, 28], "Fare": [7.25, 71.28, 7.92, 53.10, 8.05], "Pclass": [3, 1, 3, 1, 3], "Sex": ["male", "female", "female", "female", "male"], "Survived": [0, 1, 1, 1, 0] } df = pd.DataFrame(data) # Encode 'Sex' as a numeric feature df["Sex_encoded"] = df["Sex"].map({"male": 0, "female": 1}) # Create interaction features df["Age_Fare_product"] = df["Age"] * df["Fare"] df["Pclass_Sex_interaction"] = df["Pclass"] * df["Sex_encoded"] print(df[["Age", "Fare", "Pclass", "Sex", "Age_Fare_product", "Pclass_Sex_interaction", "Survived"]])
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