How to calculate F1 Score in Python?
The f1_score function from the sklearn.metrics module is used to calculate the F1 score for a multi-class classification problem. The function takes two required parameters, y_true and y_pred, and an optional parameter average. Here’s an explanation of the function and its parameters:
Python3
from sklearn.metrics import f1_scoref1_score(y_true, y_pred, average=None) |
- y_true (array-like of shape (n_samples,)): This parameter represents the true labels or ground truth for the samples. It should be an array-like structure (e.g., a list or NumPy array) containing the true class labels for each sample in the dataset.
- y_pred (array-like of shape (n_samples,)): This parameter represents the predicted labels for the samples. Like y_true, it should be an array-like structure containing the predicted class labels for each sample.
- average (string or None, default=None): This is an optional parameter that specifies the method used to calculate the F1 score for multi-class classification. It can take the following values:
- None: Returns the F1 score for each class separately. In this case, the function returns an array of shapes (n_classes,).
- ‘micro’: Calculates the F1 score globally by considering total true positives, false positives, and false negatives across all classes.
- ‘macro’: Calculates the F1 score for each class independently and then computes the unweighted average.
- ‘weighted’: Calculates the F1 score for each class independently and then computes the average weighted by the number of true instances in each class.
Python3
from sklearn.metrics import f1_score# Example datay_true = [0, 1, 2, 2, 2, 2, 1, 0, 2,1, 0]y_pred = [0, 0, 2, 2, 1 , 2, 1,0, 1,2,1]# Calculate F1 score for each class separatelyf1_per_class = f1_score(y_true, y_pred, average=None)# Calculate micro-average F1 scoref1_micro = f1_score(y_true, y_pred, average='micro')# Calculate macro-average F1 scoref1_macro = f1_score(y_true, y_pred, average='macro')# Calculate weighted-average F1 scoref1_weighted = f1_score(y_true, y_pred, average='weighted')print("F1 score per class:", f1_per_class)print("Micro-average F1 score:", f1_micro)print("Macro-average F1 score:", f1_macro)print("Weighted-average F1 score:", f1_weighted) |
Output:
F1 score per class: [0.66666667 0.28571429 0.66666667]
Micro-average F1 score: 0.5454545454545454
Macro-average F1 score: 0.5396825396825397
Weighted-average F1 score: 0.5627705627705627
F1 Score in Machine Learning
The F1 score is an important evaluation metric that is commonly used in classification tasks to evaluate the performance of a model. It combines precision and recall into a single value. In this article, we will understand in detail how the F1 score is calculated and compare it with other metrics.