metadata

title: F1
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.0.2
app_file: app.py
pinned: false
tags:
  - evaluate
  - metric
description: >-
  The F1 score is the harmonic mean of the precision and recall. It can be
  computed with the equation: F1 = 2 * (precision * recall) / (precision +
  recall)

Metric Card for F1

Metric Description

The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation: F1 = 2 * (precision * recall) / (precision + recall)

How to Use

At minimum, this metric requires predictions and references as input

>>> f1_metric = evaluate.load("f1")
>>> results = f1_metric.compute(predictions=[0, 1], references=[0, 1])
>>> print(results)
["{'f1': 1.0}"]

Inputs

predictions (list of int): Predicted labels.
references (list of int): Ground truth labels.
labels (list of int): The set of labels to include when average is not set to 'binary', and the order of the labels if average is None. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in predictions and references are used in sorted order. Defaults to None.
pos_label (int): The class to be considered the positive class, in the case where average is set to binary. Defaults to 1.
average (string): This parameter is required for multiclass/multilabel targets. If set to None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to 'binary'.
- 'binary': Only report results for the class specified by pos_label. This is applicable only if the classes found in predictions and references are binary.
- 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
- 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance. This option can result in an F-score that is not between precision and recall.
- 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
sample_weight (list of float): Sample weights Defaults to None.

Output Values

f1(float or array of float): F1 score or list of f1 scores, depending on the value passed to average. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Output Example(s):

{'f1': 0.26666666666666666}

{'f1': array([0.8, 0.0, 0.0])}

This metric outputs a dictionary, with either a single f1 score, of type float, or an array of f1 scores, with entries of type float.

Values from Popular Papers

Examples

Example 1-A simple binary example

>>> f1_metric = evaluate.load("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
>>> print(results)
{'f1': 0.5}

Example 2-The same simple binary example as in Example 1, but with pos_label set to 0.

>>> f1_metric = evaluate.load("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
>>> print(round(results['f1'], 2))
0.67

Example 3-The same simple binary example as in Example 1, but with sample_weight included.

>>> f1_metric = evaluate.load("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
>>> print(round(results['f1'], 2))
0.35

Example 4-A multiclass example, with different values for the average input.

>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
>>> print(round(results['f1'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
>>> print(round(results['f1'], 2))
0.33
>>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
>>> print(round(results['f1'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'f1': array([0.8, 0. , 0. ])}

Limitations and Bias

Citation(s)

@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}

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