F1 Score

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Goal

This post aims to introduce one of the model evaluation metrics, called F1 score. F1 score is used to measure the overall model performance. As F1 score is higher, the model performance would be better in general.

F1 score is defined as the following equations:

$$ F_1 = 2 \cdot \frac{precision \cdot recall}{precision + recall} $$

Reference

Libraries

In [10]:
from sklearn.metrics import f1_score, precision_score, recall_score
import pandas as pd

Create a prediction and ground truth

In [7]:
df_prediction = pd.DataFrame([0, 1, 0, 1, 0 ,1, 0, 1], 
                             columns=['prediction'])
df_prediction
Out[7]:
prediction
0 0
1 1
2 0
3 1
4 0
5 1
6 0
7 1
In [8]:
df_groundtruth = pd.DataFrame([0, 0, 0 , 0, 1, 1, 1, 1], 
                              columns=['gt'])
df_groundtruth
Out[8]:
gt
0 0
1 0
2 0
3 0
4 1
5 1
6 1
7 1

Compute F1 Score

In [9]:
f1_score(y_true=df_groundtruth, 
        y_pred=df_prediction)
Out[9]:
0.5

double check by precision and recall

In [11]:
precision_score(y_true=df_groundtruth, 
        y_pred=df_prediction)
Out[11]:
0.5
In [12]:
recall_score(y_true=df_groundtruth, 
        y_pred=df_prediction)
Out[12]:
0.5
In [13]:
2 * (0.5 * 0.5) / (0.5 + 0.5)
Out[13]:
0.5

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