咨询：我使用pd.get_dummies处理回归数据的方式是否正确？

Great question! Your approach using pd.get_dummies() with drop_first=True is absolutely correct for integrating categorical and numerical variables into a regression model—let’s break down why, plus some key considerations to refine your workflow:

Why drop_first=True is the right call #

When converting categorical variables to dummy variables, you risk the dummy variable trap: if a category has n distinct values, including all n dummy variables creates perfect multicollinearity (one variable can be perfectly predicted by the others). This breaks ordinary least squares (OLS) regression, as it can’t uniquely estimate coefficients for all n categories.

By setting drop_first=True, you exclude the first category of each variable, leaving n-1 dummy variables. The coefficients for these remaining variables represent the difference in the target (here, NA_Sales) relative to the excluded "reference" category—this is the standard, statistically valid way to encode categorical variables for regression.

Checking your code workflow #

Your core steps are solid, but there’s one critical detail to fix to avoid index mismatches:

• You’re dropping missing values from target_dummies with .dropna(), but you haven’t applied the same filter to features_dummies. This means your feature matrix and target vector might have different numbers of rows, which will throw an error when fitting your model.

Instead, handle missing values upfront before splitting features and target:

# Drop rows where the target has missing values first
df_clean = df_w_dummies.dropna(subset=['NA_Sales'])

# Now split features and target
features_dummies = df_clean.loc[:, df_clean.columns != 'NA_Sales']
target_dummies = df_clean['NA_Sales']

Extra tips for robust modeling #

• Handle high-cardinality categories: If a variable like Publisher has hundreds of unique values, generating dummy variables will bloat your feature space (curse of dimensionality). Consider merging rare categories into an "Other" group, or using alternative encodings like target encoding (just be careful to avoid data leakage).
• Address missing values in categorical columns: pd.get_dummies() defaults to dropna=True, which means rows with missing values in your categorical columns will have 0s in all dummy variables for that feature. If you want missing values to be treated as a distinct category, fill them first (e.g., df['Publisher'] = df['Publisher'].fillna('Unknown')) before calling get_dummies.
• Scale numerical variables (if needed): If you’re using a regularized regression model (like Ridge or Lasso), numerical variables should be standardized/normalized to ensure dummy variables (which are 0/1) don’t get unfairly weighted. For vanilla OLS, scaling isn’t strictly necessary, but it can make coefficient interpretation easier.

内容的提问来源于stack exchange，提问作者GoldenGenova