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What is K-Nearest Neighbors algorithm

K-Nearest Neighbors Algorithm

Concepts of K-Nearest Neighbors

K-Nearest Neighbors (KNN) is a non-parametric classification algorithm that works on the principle of identifying the K number of nearest neighbours of a data point in the training set and predicting its class based on the majority class of its K nearest neighbours. The value of K is a hyperparameter that needs to be optimized based on the dataset.

Here is an example of how KNN works: Suppose we have a dataset of different fruits categorized into apples, oranges, and bananas based on their weight and size. We want to classify a new fruit based on its weight and size. We choose a value of K, say K=3. We calculate the Euclidean distance between the new fruit and all the existing fruits in the dataset. We select the 3 nearest neighbours and count the number of apples, oranges, and bananas among the 3 neighbours. The class with the highest count is assigned to the new fruit.

K-Nearest Neighbors Algorithm

  • Define the problem and collect data.
  • Choose a hypothesis class (e.g., k-nearest neighbours).
  • Split the data into training and validation sets.
  • For each new data point, find the k nearest neighbours in the training set based on some distance metric.
  • Predict the class of the new data point based on the majority class of its k nearest neighbours.
  • Evaluate the model on the validation set to estimate its performance.
  • Apply the model to new data to make predictions.
non-parametric classification algorithm that works on identifying the K number of nearest neighbours of data

Here is a sample Python code for the KNN algorithm using the scikit-learn library:

python code

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import train_test_split

from sklearn.datasets import load_iris

# Load the iris dataset

iris = load_iris()

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)

# Create KNN classifier

knn = KNeighborsClassifier(n_neighbors=3)

# Train the classifier on the training data

knn.fit(X_train, y_train)

# Test the classifier on the testing data

accuracy = knn.score(X_test, y_test)

print("Accuracy: ", accuracy)

KNN Algorithm Benefits, Advantages and Disadvantages

Benefits of the KNN Algorithm: 

  • Simple to understand and implement.
  • Can work well with both binary and multi-class classification problems.
  • approach to classification and regression problems.
  • Can handle noisy data and outliers effectively.

 Advantages of the KNN Algorithm:

  • The algorithm doesn't make any assumptions about the distribution of data.
  • It can handle high-dimensional data effectively. 
  • It can work well with small datasets.
  • The algorithm is non-parametric and doesn't require any training.

 Disadvantages of the KNN Algorithm: 

  • For large datasets, the approach may be computationally expensive. 
  • The choice of K can have a significant impact on the performance of the algorithm.
  • The algorithm can be sensitive to the scaling of the data. 
  • The algorithm can be affected by the presence of irrelevant features.

Main Contents (TOPICS of Machine Learning Algorithms) 

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