Creating your own k-Nearest Neighbor model

There is always a trade off between things in life. If you take up a certain path then there is always a possibility that you might have to compromise with some other parameter. Machine Learning models are no different, considering the case of k-Nearest Neighbor there has always been a problem which has a huge impact over classifiers that rely on pairwise distance and that problem is nothing but the “Curse of Dimensionality”. By the end of this article you will be able to create your own k-Nearest Neighbor Model and observe the impact of increasing the dimension to fit a data set. Let’s dig in!

Creating a k-Nearest Neighbor model:

Right before we get our hands dirty with the technical part, we need to lay the buttress for our analysis, which is nothing but the libraries.

import matplotlib.pyplot as plt
	import matplotlib.patches as patches

	import numpy as np
	import seaborn as sns
	from scipy import stats
	sns.set_style("white")

	## for 3d plots
	from ipywidgets import interact, fixed
	from mpl_toolkits import mplot3d

	from tqdm import tqdm

	from sklearn.neighbors import KNeighborsClassifier
	from sklearn.model_selection import validation_curve, train_test_split

	plot_colors = np.array(sns.color_palette().as_hex())
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knn_lib.py hosted with ❤ by GitHub

Thanks to inbuilt machine learning packages which makes our job quite easy.

#data-science #curse-of-dimensionality #ds-in-the-real-world #data-visualization #machine-learning #data analysis