Introduction

In SVMlight, the prediction file is the output produced when you apply a trained model to new examples. It is easy to assume that this file contains only final class labels such as +1 or -1, but in practice it usually contains the model's raw prediction value for each test example. Understanding that detail is important because the sign and magnitude of the number carry meaning.

Where the prediction file comes from

The normal SVMlight workflow has two main steps:

1. train a model with svm_learn
2. apply that model with svm_classify

A typical command sequence looks like this:

svm_learn train.dat model.dat
svm_classify test.dat model.dat predictions.txt

Here:

• 'train.dat is the training set'
• 'model.dat is the learned model'
• 'test.dat is the evaluation or production input'
• 'predictions.txt is the prediction file'

The prediction file has one output line for each example in the test file, in the same order.

What the values usually mean

For standard binary classification, SVMlight typically writes the raw decision function value, not just the hard class label. That means:

• a positive value predicts the positive class
• a negative value predicts the negative class
• a value near zero means the example is near the separating hyperplane

For example, a prediction file might look like this:

12.341
2-0.882
30.104
4-3.912

The first and third examples would be classified as positive, and the second and fourth as negative. The last example is farther from the boundary than the third, so the model is making a stronger signed decision there.

This is one reason the prediction file is more useful than a plain label file. It preserves ranking information.

Why raw scores matter

Those numeric values are often used for more than accuracy measurement. They are useful when you want to:

• rank examples by confidence-like score
• compute precision and recall at different thresholds
• compare positives and negatives by margin
• feed downstream evaluation scripts

In other words, the prediction file is often closer to "decision scores" than "final human-readable answers."

Interpreting predictions safely

The exact interpretation depends on the learning task:

• in binary classification, the sign usually determines the class
• in regression mode, the file represents predicted numeric outputs instead

That is why you should always interpret the file in the context of how the model was trained. A prediction file from a regression run is not a signed class-margin file.

Here is a small Python example that reads a binary-classification prediction file and turns it into labels:

1from pathlib import Path
2
3
4def load_svmlight_predictions(path: str) -> list[tuple[float, int]]:
5    rows: list[tuple[float, int]] = []
6
7    for line in Path(path).read_text().splitlines():
8        score = float(line.strip())
9        label = 1 if score > 0 else -1
10        rows.append((score, label))
11
12    return rows
13
14
15for score, label in load_svmlight_predictions("predictions.txt"):
16    print(f"score={score:.3f}, predicted_label={label}")

This keeps both pieces of information: the raw score and the derived class label.

Relation to evaluation

If you also have the true labels for the same test set, you can compare them against the prediction file line by line. Because order is preserved, the first prediction corresponds to the first test example, the second to the second example, and so on.

That alignment is why accidental reshuffling of the test file is a serious mistake. The prediction file itself does not repeat the input ID unless you add that bookkeeping outside SVMlight.

Common Pitfalls

The most common mistake is thinking the prediction file always contains final class labels only. In standard binary classification, it is usually more informative than that because it stores signed decision values.

Another issue is forgetting that line order matters. If the test data is reordered after prediction, the file no longer lines up with the original examples.

People also compare scores from different models too casually. The sign is generally safe for class direction, but score magnitudes are not always directly comparable across differently trained models.

Finally, remember that regression output is interpreted differently from classification output. The same file format can be used for different prediction meanings.

Summary

• The prediction file is the output of svm_classify when a trained SVMlight model is applied to new data.
• It contains one line per test example, in the same order as the input file.
• For binary classification, the file usually stores signed decision values rather than just hard labels.
• Positive and negative signs indicate predicted class direction, and magnitude reflects distance from the boundary.
• Always interpret the file in the context of whether the model was trained for classification or regression.