Page 233 - AI_Ver_3.0_class_11
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# Calculate accuracy
accuracy = metrics.accuracy_score(y_test, y_pred)
# Calculate precision
precision = metrics.precision_score(y_test, y_pred, average='weighted')
print("Accuracy:", accuracy)
print("Precision:", precision)
Output:
Accuracy: 1.0
Precision: 1.0
Now, if you want to validate the predictive accuracy of the model based on the sample data. For example:
[[3, 5, 4, 2], [2, 2, 5, 4]]
Program 64: To evaluate the metrics of the IRIS dataset based on the sample data for testing
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)
# Calculate accuracy
accuracy = metrics.accuracy_score(y_test, y_pred)
# Make a prediction based on the new sample data
sample = [[3, 5, 4, 2], [2, 2, 5, 4]]
prediction = knn.predict(sample)
prediction_species = [iris.target_names[p] for p in prediction]
print("Accuracy:", accuracy)
print("Predictions:", prediction_species)
Python Programming 231

