机器学习中的特征能否为矩阵？是否可使用二维数组作为特征？

Absolutely! Machine learning features can absolutely be matrices (or even higher-dimensional arrays)—this isn’t just allowed, it’s standard practice for many types of data and tasks. Let’s break this down clearly:

Core Context: From 1D to 2D Features #

The Iris dataset you mentioned uses 1D feature vectors (like [5.1, 3.5, 1.4, 0.2]) because it’s a classic tabular dataset—each sample is a set of independent numerical attributes. But this is just one type of feature representation. Many real-world datasets naturally have 2D structure that’s critical to preserve for good model performance.

Common Use Cases for 2D Features #

Here are some everyday examples where 2D features are the norm:

• Image Data: Think of MNIST handwritten digits—each sample is a 28x28 grayscale matrix, where each element represents a pixel’s brightness. The spatial relationship between pixels (e.g., how adjacent pixels form a digit’s shape) is key to the task, so we keep the 2D structure instead of flattening it.
• Time Series with Context: If you’re working with sensor data (like temperature readings over time), you might create 2D features by taking sliding windows of 10 time steps, each with 3 sensor values—resulting in a 10x3 matrix per sample. This captures temporal patterns that a 1D vector would miss.
• Text with Word Embeddings: When processing text, you might represent a sentence as a matrix where each row is a word embedding vector (e.g., from common pre-trained models). The order of words (row order) and the vector values carry semantic meaning that’s lost if you flatten the matrix.

Your Example is Totally Valid #

The 2D feature samples you provided:

Feature                  Result
[[1.0 2.3 3.1],          A
 [2.4 6.3 9.6]]
[[1.5 3.3 5.1],          B
 [5.4 9.3 7.0]]

are completely acceptable. You have a few options for using them:

1. Keep the 2D structure: Use models designed for multidimensional input, like Convolutional Neural Networks (CNNs) which excel at extracting patterns from grid-like data, or Recurrent Neural Networks (RNNs) if the rows have a sequential relationship.
2. Flatten to 1D: If you want to use a simpler model like Logistic Regression or SVM, you can convert each 2x3 matrix into a 1D vector (e.g., [1.0, 2.3, 3.1, 2.4, 6.3, 9.6]). Just note that this discards any structural relationships between rows/columns, so only do this if that structure isn’t important for your task.

Final Takeaway #

Feature dimensions aren’t limited to 1D—they’re determined by your data’s inherent structure and what your model needs to learn. 1D works for tabular data, 2D (or higher) works for data with spatial, temporal, or hierarchical structure. As long as your model can accept the input shape, you’re good to go!

内容的提问来源于stack exchange，提问作者Trung