defaultdict与get_dummies编码分类变量的差异及前者劣势咨询

Great question! Let's break down why this LabelEncoder approach has notable drawbacks compared to pd.get_dummies for most categorical encoding tasks:

• Incorrectly introduces ordinal relationships
  LabelEncoder maps categorical values to integers (e.g., "red" → 0, "blue" → 1, "green" → 2) which implies an ordinal hierarchy (like green > blue > red) that often doesn't exist for unordered categories (e.g., colors, brands). Most models (linear regression, SVM, etc.) will interpret these integers as continuous values with inherent order, leading to misleading feature weights and wrong model assumptions. pd.get_dummies uses one-hot encoding, creating separate binary features for each category—no false ordinal relationships here.

• Poor compatibility with non-tree-based models
  Tree-based models (decision trees, random forests) can sometimes handle ordinal encoded features without major issues, but linear models, neural networks, and support vector machines rely heavily on feature scale and semantic meaning. The integer outputs from LabelEncoder will skew how these models learn feature importance and make predictions. One-hot encoding is the standard for these model types because it treats each category as an independent, equally weighted feature.

• Worse interpretability and lost category-specific insights
  With pd.get_dummies, you can directly analyze how individual categories impact your model's predictions (e.g., "customers from New York have a 0.2 higher predicted churn rate"). LabelEncoder collapses all categories into a single integer feature—you can't easily isolate the effect of any one category, making model debugging and interpretation far harder.

• Higher risk of errors with unseen categories in test data
  Your code saves a LabelEncoder per column, but if your test set contains a category that wasn't present in the training data, LabelEncoder.transform() will throw an error. While pd.get_dummies can also struggle with unseen categories, it's easier to handle (e.g., drop the new category column, or predefine all possible categories upfront). You'd need extra code to handle unseen values with the LabelEncoder approach, which adds complexity.

• Less efficient for high-cardinality categories
  When you have a category with many unique values, one-hot encoding creates a sparse matrix (most values are 0), which many ML frameworks optimize for to save memory. LabelEncoder outputs a dense integer array, which uses more memory for high-cardinality features and doesn't leverage sparse data optimizations.

内容的提问来源于stack exchange，提问作者user5768866