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# Face Recognition using KNN

A supervised machine learning algorithm is one that relies on labeled input data to learn a function that produces an appropriate output when given new unlabeled data. ex => K Nearest Neighbours

KNN is a lazy learning, non-parametric algorithm. It uses data with several classes to predict the classification of the new sample point. KNN is non-parametric since it doesn’t make any assumptions on the data being studied, i.e., the model is distributed from the data.

KNN is very simple and is often used as a benchmark for more complex classifiers like the Support Vector Machines (SVM) and the Artificial Neural Networks (ANN).

# Working of KNN Algorithm in Machine Learning

To understand better the working KNN algorithm applies the following steps when using it:

Step 1 — When implementing an algorithm, you will always need a data set. So, you start by loading the training and the test data.

Step 2 — Choose the nearest data points (the value of K). K can be any integer.

Step 3 — Do the following, for each test data –

3.1 — Use Euclidean distance, Hamming, or Manhattan to calculate the distance between test data and each row of training. The Euclidean method is the most used when calculating distance.

3.2 — Sort data set in ascending order based on the distance value.

3.3 — From the sorted array, choose the top K rows.

3.4 — Based on the most appearing class of these rows, it will assign a class to the test point.

Step 4 — End

‘Birds of same feather flock together’ well justifies KNN algorithm.

# KNN into play

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