Python Algorithm Implementation from scipy import stats from pylab import * def knnClassify(Target_feature, dataSet, p, k): dataSet_x = dataSet[:, 0:-1] dataSet_y = dataSet[:, -1] L_p = [] for i in range(len(dataSet_x)): l_p = 0 for x_i in range(len(dataSet_x[i])): l_p = (abs(dataSet_x[i][x_i] – Target_feature[x_i]))**p+l_p l_p =math.pow(l_p, 1/p) L_p.append(l_p) L_p = np.array(L_p) y_i = L_p.argsort() y = … Read more
Source: DeepHub IMBA This article is approximately 1200 words long and is recommended for a 5-minute read. In this article, we will introduce the PyOD package and provide detailed code examples. Outlier detection is a key task in various fields. PyOD, which stands for Python Outlier Detection, simplifies the process of identifying outliers in multivariate … Read more
Recently, I have been reading the book “Machine Learning in Action” because I want to delve deeper into machine learning algorithms, and since I want to learn Python, I chose this book to study based on my friend’s recommendation. Before writing this article, I had some understanding of FCM, so I feel an inexplicable affinity … Read more
Author: AISHWARYA SINGH, AUGUST 22, 2018 Translator: ZHAO XUEYAO Proofreader: ZHANG LING This article is approximately 4200 words, and is recommended for a 10 minute read. This article explains the basic working principle of the K-Nearest Neighbors (KNN) algorithm and briefly introduces three methods for calculating the distance between points. Introduction Among all the machine … Read more
K-Nearest Neighbor (KNN) classification algorithm is a theoretically mature method and one of the simplest machine learning algorithms. The idea behind this method is: in the feature space, if the majority of the k nearest samples (i.e., the closest samples in the feature space) around a sample belong to a certain category, then that sample … Read more
This article introduces one of the most basic and also the “laziest” algorithms in machine learning—KNN (k-nearest neighbor). Do you know why it is considered the laziest? 01|Algorithm Overview: KNN is short for k-nearest neighbor, which refers to the K closest points. This algorithm is commonly used to solve classification problems. The specific principle of … Read more
KNN (K-Nearest Neighbors) algorithm is a simple and intuitive supervised learning algorithm, widely used for classification and regression tasks. This article will guide you step by step on how to implement the KNN algorithm using the scikit-learn library in Python, and we will practice with the Iris dataset. Let’s explore how to classify the Iris … Read more
1. Basic Concepts The K-nearest neighbors method (KNN) can be used for both classification and regression. The difference between KNN for regression and classification lies in the decision-making process during the final prediction. When KNN is used for classification, it generally employs a majority voting method. When KNN is used for regression, it typically uses … Read more
The KNN algorithm is one of the most basic instance-based learning methods. First, we will introduce the relevant concepts of instance-based learning. 1. Instance-Based Learning 1. Given a series of training samples, many learning methods establish a clear generalization description for the objective function; however, instance-based learning methods simply store the training samples. The work … Read more
Source: DeepHub IMBA This article is about 1700 words, and it is recommended to read for 7minutes. This article compares KNN with simple linear regression. Generally, k-Nearest Neighbor (KNN) is used to solve classification problems; in fact, KNN is a simple algorithm that can be applied to both data classification and prediction. In this article, … Read more