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
K-Nearest Neighbors Underfitting and Overfitting
KNN Classification Algorithm in Scikit Learn
Euclidean Distance
Elbow Curve Validation Technique in K-Nearest Neighbor Algorithm
K-Nearest Neighbors
KNN of Unknown Data Point
Normalizing Data
K-Nearest Neighbors Underfitting and Overfitting
K-Nearest Neighbors Underfitting and Overfitting
The value of k in the KNN algorithm is related to the error rate of the model. A small value of k could lead to overfitting as well as a big value of k can lead to underfitting. Overfitting imply that the model is well on the training data but has poor performance when new data is coming. Underfitting refers to a model that is not good on the training data and also cannot be generalized to predict new data.
What you'll create
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How you'll master it
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Classification: K-Nearest Neighbors
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