Classification: K-Nearest Neighbors
K-Nearest Neighbors is a supervised machine learning algorithm for classification. You will implement and test this algorithm on several datasets.
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Key Concepts
Review core concepts you need to learn to master this subject
KNN Classification Algorithm in Scikit Learn
K-Nearest Neighbors Underfitting and Overfitting
Euclidean Distance
Elbow Curve Validation Technique in K-Nearest Neighbor Algorithm
K-Nearest Neighbors
KNN of Unknown Data Point
Normalizing Data
KNN Classification Algorithm in Scikit Learn
KNN Classification Algorithm in Scikit Learn
from sklearn.neighbors import KNeighborsClassifier
KNNClassifier = KNeighborsClassifier(n_neighbors=5)
KNNClassifier.fit(X_train, y_train)
KNNClassifier.predict(X_test)Scikit-learn is a very popular Machine Learning library in Python which provides a KNeighborsClassifier object which performs the KNN classification. The n_neighbors parameter passed to the KNeighborsClassifier object sets the desired k value that checks the k closest neighbors for each unclassified point.
The object provides a .fit() method which takes in training data and a .predict() method which returns the classification of a set of data points.
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Classification: K-Nearest Neighbors
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