自学机器学习：二元分类任务中标签编码方式的合理性咨询

Great question—let’s cut through the confusion here, since this is a common point of uncertainty when starting out with classification tasks.

First, let’s get straight to the answer: For most standard binary classification scenarios (labels as 0/1 representing False/True), you do NOT need to convert to two separate variables (like variable_true and variable_false) via one-hot encoding. Here’s why:

• Most ML algorithms are designed natively for binary 0/1 labels
  Algorithms like logistic regression, support vector machines (SVM), random forests, and gradient-boosted trees all handle 0/1 binary labels perfectly. The numerical values here are just a clear, concise way to represent the two mutually exclusive classes—no extra information is gained by splitting them into two columns. In fact, splitting creates redundant data (if variable_true is 1, variable_false is guaranteed to be 0) which can introduce unnecessary multicollinearity, leading to issues with some models (like linear regression variants).

• Dummy variables for binary classification are exactly what you’re already using
  When people talk about "dummy variables" for categorical data, for a binary category, it’s precisely the single-column 0/1 representation you have. Dummy encoding for k categories creates k-1 columns to avoid redundancy—so for 2 classes, that’s 1 column, not 2.

When would you need one-hot encoding?
The main exception is if you’re using a deep learning framework with a categorical cross-entropy loss function, which expects one-hot encoded labels for multi-class tasks. But even then, for binary classification, you can use binary cross-entropy instead, which works directly with 0/1 labels. If you do need one-hot for some specific tool or loss function, you can convert it, but it’s not required for most standard workflows.

Final Takeaway #

Stick with your current single-column 0/1 label format—it’s efficient, widely supported, and avoids unnecessary complexity. Only consider converting if your specific model or training setup explicitly requires multi-column one-hot labels.

内容的提问来源于stack exchange，提问作者David