Accuracy
Description
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:
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.
Python
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(digits.data, digits.target, test_size=0.2, random_state=0)
clf = LogisticRegression(max_iter=10000)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
ACC = accuracy_score(y_test, y_pred)
print(f"Accuracy: {ACC}")