The overall correctness of the classifier can be determined by the accuracy. The latter is defines the ratio of the correct predictions to the total number of predections as follows:

\[\text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}}\]

Tools and Libraries

To illustrate the classification metrics, the digits dataset provided by the scikit-learn library will be used. It is a dataset of hand-written digits which contains 1797 samples and each sample is an 8x8-image. The number of classes in this dataset is 10 (corresponding to the digits from 0 to 9). This dataset will be loaded and split in train and test test (20%) sets. As a classifier, a logistic regression model will be used and trained. After the training, the model’s performance will be evaluated.


To compute the accuracy for a classification problem, the following example can be used.

Install scikit-learn using this command:

pip install -U scikit-learn
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.2, random_state=0)

clf = LogisticRegression(max_iter=10000), y_train)
y_pred = clf.predict(X_test)

ACC = accuracy_score(y_test, y_pred)
print(f"Accuracy: {ACC}")