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Number of correctly classified instances
Accuracy =
Total number of instances
• • Confusion matrix: A confusion matrix provides a breakdown of correct and incorrect classifications for each class.
Predicted Negative Predicted Positive
Actual Negative True Negative (TN) False Positive (FP)
Actual Positive False Negative (FN) True Positive (TP)
• • Precision: Precision measures the accuracy of positive predictions. It is the ratio of correctly predicted positive
observations to the total predicted positives.
TP
Precision =
TP + FP
Precision matrix would be most appropriate to use when the cost of false positives is high.
Before evaluating the metrics, you need to first import it by using the following code:
from sklearn import metrics
In Python, the metrics.precision_score() method is used to calculate the precision of a classification model in Python
using the sklearn library.
Let us now evaluate the metrics.
Program 63: To evaluate the metrics of the IRIS dataset after KNN classification
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
# load dataset
iris = load_iris()
# separate the data into features and target
X = iris.data
y = iris.target
# Split the data: 80% for training, 20% for testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_
state=1)
# Splitting the data into training and testing sets (80% training, 20% testing)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_
state=1)
# Create a KNN classifier with 3 neighbors
knn = KNeighborsClassifier(n_neighbors=3)
# Train the KNN classifier on the training data
knn.fit(X_train, y_train)
# Use the trained classifier to make predictions on the test data
y_pred = knn.predict(X_test)
230 Touchpad Artificial Intelligence (Ver. 3.0)-XI

