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Learn KNN | Recognizing Handwritten Digits
Recognizing Handwritten Digits

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KNN

The K-Nearest Neighbors (KNN) algorithm, a supervised machine learning technique, is predominantly utilized for classification. This algorithm operates by classifying a new data point according to the categories of its closest neighbors within the training dataset.

In the context of classification, the KNN classifier designates a class to a new data point by identifying the 'k' nearest neighbors in the training set, with 'k' being a user-defined parameter. The classification of the new data point is then determined by a majority vote among these 'k' nearest neighbors.

Despite its simplicity and adaptability, the KNN algorithm is computationally intensive for extensive datasets. It necessitates a meticulous selection of both the 'k' value and the distance metric. Nonetheless, KNN remains a widely employed and effective tool for classification tasks in the realm of machine learning.

Task

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  1. Initialize a K-Nearest Neighbors classifier with 4 neighbors.

  2. Train the classifier with the training data and the corresponding labels.

  3. Predict classes for the test set using the trained classifier.

Solution

from sklearn.neighbors import KNeighborsClassifier

# Initialize the K-Nearest Neighbors classifier with 4 neighbors
classifier_1 = KNeighborsClassifier(n_neighbors=4)

# Train the classifier using the training data and corresponding labels
classifier_1.fit(X_train, y_train)

# Use the trained classifier to predict the classes for the test set
Y_pred = classifier_1.predict(X_test)

# Print the predicted classes for the test set
print('Predicted classes:', Y_pred)

# Plot the distribution of predicted classes using a count plot
plt.title('Prediction Distribution')
sns.countplot(x=Y_pred)
plt.show()

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Section 1. Chapter 7
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