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K Nearest Neighbors

The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.

Knn 160904075605-converted

This document discusses the K-nearest neighbors (KNN) algorithm, an instance-based learning method used for classification. KNN works by identifying the K training examples nearest to a new data point and assigning the most common class among those K neighbors to the new point. The document covers how KNN calculates distances between data points, chooses the value of K, handles feature normalization, and compares strengths and weaknesses of the approach. It also briefly discusses clustering, an unsupervised learning technique where data is grouped based on similarity.

Lecture slides week14-15

This document discusses distance measures and scales of measurement that are important for k-nearest neighbor classification. It covers two major classes of distance measures - Euclidean and non-Euclidean. It also describes three major scales of measurement for data - nominal, ordinal, and interval scales. It provides examples of different distance functions like Euclidean, Manhattan, cosine, and edit distances. It discusses how the choice of distance measure depends on the type of data and its scale of measurement.

K-Nearest Neighbor(KNN)

The K-Nearest Neighbors (KNN) algorithm is a robust and intuitive machine learning method employed to tackle classification and regression problems. By capitalizing on the concept of similarity, KNN predicts the label or value of a new data point by considering its K closest neighbours in the training dataset. In this article, we will learn about a supervised learning algorithm (KNN) or the k – Nearest Neighbours, highlighting it’s user-friendly nature.
What is the K-Nearest Neighbors Algorithm?
K-Nearest Neighbours is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining, and intrusion detection.
It is widely disposable in real-life scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about the distribution of data (as opposed to other algorithms such as GMM, which assume a Gaussian distribution of the given data). We are given some prior data (also called training data), which classifies coordinates into groups identified by an attribute.

Data Mining Lecture_10(b).pptx

Lecture 10b: Classification. k-Nearest Neighbor classifier, Logistic Regression, Support Vector Machines (SVM), Naive Bayes (ppt,pdf)
Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.

K- Nearest Neighbor Approach

The document discusses K-Nearest Neighbor (KNN) algorithm as a supervised classification approach. KNN is a non-parametric technique that classifies a query instance based on its similarity to training examples. It finds the K closest neighbors of the query instance and predicts the class based on a majority vote of its neighbors' classes. The value of K is an important hyperparameter that affects classification accuracy. Larger K values generally improve accuracy by smoothing the decision boundary.

KNN

The document discusses the K-nearest neighbor (K-NN) classifier, a machine learning algorithm where data is classified based on its similarity to its nearest neighbors. K-NN is a lazy learning algorithm that assigns data points to the most common class among its K nearest neighbors. The value of K impacts the classification, with larger K values reducing noise but possibly oversmoothing boundaries. K-NN is simple, intuitive, and can handle non-linear decision boundaries, but has disadvantages such as computational expense and sensitivity to K value selection.

K nearest neighbours

In the four years of my data science career, I have built more than 80% classification models and just 15-20% regression models. These ratios can be more or less generalized throughout the industry. The reason behind this bias towards classification models is that most analytical problems involve making a decision.
For instance, will a customer attrite or not, should we target customer X for digital campaigns, whether a customer has a high potential or not, etc. These analyses are more insightful and directly linked to an implementation roadmap.

K Nearest Neighbors

The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.

Knn 160904075605-converted

This document discusses the K-nearest neighbors (KNN) algorithm, an instance-based learning method used for classification. KNN works by identifying the K training examples nearest to a new data point and assigning the most common class among those K neighbors to the new point. The document covers how KNN calculates distances between data points, chooses the value of K, handles feature normalization, and compares strengths and weaknesses of the approach. It also briefly discusses clustering, an unsupervised learning technique where data is grouped based on similarity.

Lecture slides week14-15

This document discusses distance measures and scales of measurement that are important for k-nearest neighbor classification. It covers two major classes of distance measures - Euclidean and non-Euclidean. It also describes three major scales of measurement for data - nominal, ordinal, and interval scales. It provides examples of different distance functions like Euclidean, Manhattan, cosine, and edit distances. It discusses how the choice of distance measure depends on the type of data and its scale of measurement.

K-Nearest Neighbor(KNN)

The K-Nearest Neighbors (KNN) algorithm is a robust and intuitive machine learning method employed to tackle classification and regression problems. By capitalizing on the concept of similarity, KNN predicts the label or value of a new data point by considering its K closest neighbours in the training dataset. In this article, we will learn about a supervised learning algorithm (KNN) or the k – Nearest Neighbours, highlighting it’s user-friendly nature.
What is the K-Nearest Neighbors Algorithm?
K-Nearest Neighbours is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining, and intrusion detection.
It is widely disposable in real-life scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about the distribution of data (as opposed to other algorithms such as GMM, which assume a Gaussian distribution of the given data). We are given some prior data (also called training data), which classifies coordinates into groups identified by an attribute.

Data Mining Lecture_10(b).pptx

Lecture 10b: Classification. k-Nearest Neighbor classifier, Logistic Regression, Support Vector Machines (SVM), Naive Bayes (ppt,pdf)
Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.

K- Nearest Neighbor Approach

The document discusses K-Nearest Neighbor (KNN) algorithm as a supervised classification approach. KNN is a non-parametric technique that classifies a query instance based on its similarity to training examples. It finds the K closest neighbors of the query instance and predicts the class based on a majority vote of its neighbors' classes. The value of K is an important hyperparameter that affects classification accuracy. Larger K values generally improve accuracy by smoothing the decision boundary.

KNN

The document discusses the K-nearest neighbor (K-NN) classifier, a machine learning algorithm where data is classified based on its similarity to its nearest neighbors. K-NN is a lazy learning algorithm that assigns data points to the most common class among its K nearest neighbors. The value of K impacts the classification, with larger K values reducing noise but possibly oversmoothing boundaries. K-NN is simple, intuitive, and can handle non-linear decision boundaries, but has disadvantages such as computational expense and sensitivity to K value selection.

K nearest neighbours

In the four years of my data science career, I have built more than 80% classification models and just 15-20% regression models. These ratios can be more or less generalized throughout the industry. The reason behind this bias towards classification models is that most analytical problems involve making a decision.
For instance, will a customer attrite or not, should we target customer X for digital campaigns, whether a customer has a high potential or not, etc. These analyses are more insightful and directly linked to an implementation roadmap.

KNN presentation.pdf

The document discusses nearest neighbor classifiers. It begins by explaining that nearest neighbor classifiers store all training records and classify unseen cases based on the class labels of the k closest records. It then provides more details on how nearest neighbor classifiers work, including that they require a distance metric to calculate distances between records and a value of k for the number of nearest neighbors. To classify an unknown record, the k nearest neighbors are identified and their class labels are used to determine the class, such as by majority vote. Issues like choosing k, scaling attributes, and high-dimensional data are also addressed.

Knn

KNN is a simple machine learning algorithm that classifies new data points based on their similarity to existing stored data points. It works by finding the k nearest neighbors of the new data point based on a distance measure and assigning the most common class (for classification) or average value (for regression) of the k nearest neighbors. Though conceptually simple, KNN can solve complex problems with little information needed and no explicit training. However, it requires storing all training data and is sensitive to representation and feature selection.

Knn

KNN is a simple machine learning algorithm that classifies new data points based on their similarity to existing stored data points. It works by finding the k nearest neighbors of the new data point based on a distance measure and assigning the most common class (for classification) or average value (for regression) of the k nearest neighbors. Though conceptually simple, KNN can solve complex problems with little information needed and no explicit training. However, it requires storing all training data and is sensitive to representation and feature selection.

Classification_Algorithms_Student_Data_Presentation

The document describes a study that aims to predict student academic achievement using classification algorithms. It explains that two algorithms, K-Nearest Neighbors (KNN) and Naive Bayes (NB), were implemented on student data to classify students as having low, medium, or high academic performance. The algorithms were run under different parameter configurations and their results were analyzed. The document concludes by discussing limitations of the study and ways the analysis could be expanded.

07 learning

This document contains legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance depends on system configuration and that sample source code is released under an Intel license agreement. Finally, it provides basic copyright information.

Clasification approaches

This document provides an overview of classification approaches and techniques for improving classification accuracy. It discusses decision tree induction, Bayesian classification methods, and rule-based classification. It also covers model evaluation and selection as well as ensemble methods. Additionally, it defines key terminology related to classification including supervised vs. unsupervised learning, classification vs. numeric prediction, and the two-step classification process of model construction and model usage.

knn-1.pptx

The document discusses the K-Nearest Neighbors (KNN) algorithm. KNN is a classification algorithm that stores all available cases and classifies new cases based on similarity, measured by a distance function. It works by finding the K training examples nearest to the new instance, and predicting the class as the most common class among those K examples. The document provides an example predicting the winner of a cricket match between India and Pakistan using KNN, calculating distances between instances and predicting based on the majority vote of the 3 nearest neighbors.

Knn

This document discusses K-nearest neighbors (KNN) classification. KNN is an instance-based learning algorithm that stores all available cases and classifies new cases based on a vote of its neighbors. It explains how KNN classifies points based on the majority vote of its K closest neighbors, where closeness is typically defined using Euclidean or Hamming distance. It also discusses how to choose the K value, the pros and cons of KNN, and applications where KNN is often used.

DBSCAN (2014_11_25 06_21_12 UTC)

DBSCAN is a density-based clustering algorithm that can find clusters of arbitrary shape. It requires two parameters: epsilon, which defines the neighborhood distance, and minimum points. It marks points as core, border or noise based on the number of points within their epsilon-neighborhood. Randomized DBSCAN improves the time complexity from O(n^2) to O(n) by randomly selecting a maximum of k points in each neighborhood to analyze rather than all points. Testing shows Randomized DBSCAN performs as well as DBSCAN in terms of accuracy while improving runtime, especially at higher data densities relative to epsilon. Future work includes analyzing accuracy in higher dimensions and combining with indexing to further improve time complexity.

Measures of Dispersion.pptx

This document discusses various measures of dispersion used to describe how spread out or clustered data values are around a central measure like the mean or median. It defines absolute and relative measures of dispersion and explains key measures like range, interquartile range, quartile deviation, mean deviation, and their coefficients. Examples are provided to demonstrate calculating each measure for both ungrouped and grouped data. The advantages and disadvantages of range, quartile deviation, and mean deviation are also outlined.

K Nearest neighbour

This document discusses nearest neighbor classification, which is an instance-based machine learning algorithm. It stores all training records and classifies new records based on their similarity to stored examples, by finding the k nearest neighbors and predicting the class as the most common label among those neighbors. Key aspects covered include using a distance metric like Euclidean distance to find neighbors, choosing an optimal k value, addressing scaling issues between attributes, and challenges with high-dimensional data.

Knn

K-Nearest Neighbor (KNN) is a supervised machine learning algorithm that can be used for classification and prediction. It finds the K closest training examples to a new data point and assigns the most common class among those K examples to the new data point. Euclidean distance is often used to calculate the distance between points. An example is provided of classifying a new paper sample as good or bad based on acid durability and strength attributes by finding its 3 nearest neighbors and assigning it the majority class.

TunUp final presentation

This document summarizes a distributed cloud-based genetic algorithm framework called TunUp for tuning the parameters of data clustering algorithms. TunUp integrates existing machine learning libraries and implements genetic algorithm techniques to tune parameters like K (number of clusters) and distance measures for K-means clustering. It evaluates internal clustering quality metrics on sample datasets and tunes parameters to optimize a chosen metric like AIC. The document outlines TunUp's features, describes how it implements genetic algorithms and parallelization, and concludes it is an open solution for clustering algorithm evaluation, validation and tuning.

Fa18_P2.pptx

This document discusses density-based clustering algorithms. It begins by outlining the limitations of k-means clustering, such as its inability to find non-convex clusters or determine the intrinsic number of clusters. It then introduces DBSCAN, a density-based algorithm that can identify clusters of arbitrary shapes and handle noise. The key definitions and algorithm of DBSCAN are described. While effective, DBSCAN relies on parameter selection and cannot handle varying densities well. OPTICS is then presented as an augmentation of DBSCAN that produces a reachability plot to provide insight into the underlying cluster structure and avoid specifying the cluster count.

Part 2

The document discusses DBSCAN, a density-based clustering algorithm. DBSCAN identifies core points that have many nearby points and clusters are formed based on core point neighborhoods. It defines core, border and noise points based on minimum point and epsilon parameters. The time complexity of DBSCAN is O(n^2) in the worst case. Internal validation measures like sum of squared error (SSE) and external measures like entropy can evaluate clustering results. The similarity matrix approach also allows visual validation of clusters.

Variability

The document discusses variability and measures of variability. It defines variability as a quantitative measure of how spread out or clustered scores are in a distribution. The standard deviation is introduced as the most commonly used measure of variability, as it takes into account all scores in the distribution and provides the average distance of scores from the mean. Properties of the standard deviation are examined, such as how it does not change when a constant is added to all scores but does change when all scores are multiplied by a constant.

k-Nearest Neighbors with brief explanation.pptx

Completely explained about knn algorithm I'm ml

introduction to machine learning 3c-feature-extraction.pptx

This document discusses feature extraction and dimensionality reduction techniques. It begins by defining feature extraction as mapping a set of features to a reduced feature set that maximizes classification ability. It then explains principal component analysis (PCA) and how it works by finding orthogonal directions that maximize data variance. However, PCA does not consider class information. Linear discriminant analysis (LDA) is then introduced as a technique that finds projections by maximizing between-class distance and minimizing within-class distance to better separate classes. LDA thus provides a "good projection" for classification tasks.

Data Mining Lecture_5.pptx

Lecture 5: Similarity and Distance. Metrics. Min-wise independent hashing. (ppt,pdf)
Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
Chapter 2 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.

similarities-knn.pptx

Installing and configuring computer systems involves competencies to assemble computer hardware, install operating systems and drivers for peripherals/devices, and install application software as well as to conduct testing and documentation.
This learning activity sheet will help you understand better the concepts and skills of installing and configuring computer systems. Try your best to go over the activities and answer the questions or items asked for.
Learning

132/33KV substation case study Presentation

132/33Kv substation case study ppt

Digital Twins Computer Networking Paper Presentation.pptx

A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.

KNN presentation.pdf

The document discusses nearest neighbor classifiers. It begins by explaining that nearest neighbor classifiers store all training records and classify unseen cases based on the class labels of the k closest records. It then provides more details on how nearest neighbor classifiers work, including that they require a distance metric to calculate distances between records and a value of k for the number of nearest neighbors. To classify an unknown record, the k nearest neighbors are identified and their class labels are used to determine the class, such as by majority vote. Issues like choosing k, scaling attributes, and high-dimensional data are also addressed.

Knn

KNN is a simple machine learning algorithm that classifies new data points based on their similarity to existing stored data points. It works by finding the k nearest neighbors of the new data point based on a distance measure and assigning the most common class (for classification) or average value (for regression) of the k nearest neighbors. Though conceptually simple, KNN can solve complex problems with little information needed and no explicit training. However, it requires storing all training data and is sensitive to representation and feature selection.

Classification_Algorithms_Student_Data_Presentation

The document describes a study that aims to predict student academic achievement using classification algorithms. It explains that two algorithms, K-Nearest Neighbors (KNN) and Naive Bayes (NB), were implemented on student data to classify students as having low, medium, or high academic performance. The algorithms were run under different parameter configurations and their results were analyzed. The document concludes by discussing limitations of the study and ways the analysis could be expanded.

07 learning

This document contains legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance depends on system configuration and that sample source code is released under an Intel license agreement. Finally, it provides basic copyright information.

Clasification approaches

This document provides an overview of classification approaches and techniques for improving classification accuracy. It discusses decision tree induction, Bayesian classification methods, and rule-based classification. It also covers model evaluation and selection as well as ensemble methods. Additionally, it defines key terminology related to classification including supervised vs. unsupervised learning, classification vs. numeric prediction, and the two-step classification process of model construction and model usage.

knn-1.pptx

The document discusses the K-Nearest Neighbors (KNN) algorithm. KNN is a classification algorithm that stores all available cases and classifies new cases based on similarity, measured by a distance function. It works by finding the K training examples nearest to the new instance, and predicting the class as the most common class among those K examples. The document provides an example predicting the winner of a cricket match between India and Pakistan using KNN, calculating distances between instances and predicting based on the majority vote of the 3 nearest neighbors.

Knn

This document discusses K-nearest neighbors (KNN) classification. KNN is an instance-based learning algorithm that stores all available cases and classifies new cases based on a vote of its neighbors. It explains how KNN classifies points based on the majority vote of its K closest neighbors, where closeness is typically defined using Euclidean or Hamming distance. It also discusses how to choose the K value, the pros and cons of KNN, and applications where KNN is often used.

DBSCAN (2014_11_25 06_21_12 UTC)

DBSCAN is a density-based clustering algorithm that can find clusters of arbitrary shape. It requires two parameters: epsilon, which defines the neighborhood distance, and minimum points. It marks points as core, border or noise based on the number of points within their epsilon-neighborhood. Randomized DBSCAN improves the time complexity from O(n^2) to O(n) by randomly selecting a maximum of k points in each neighborhood to analyze rather than all points. Testing shows Randomized DBSCAN performs as well as DBSCAN in terms of accuracy while improving runtime, especially at higher data densities relative to epsilon. Future work includes analyzing accuracy in higher dimensions and combining with indexing to further improve time complexity.

Measures of Dispersion.pptx

This document discusses various measures of dispersion used to describe how spread out or clustered data values are around a central measure like the mean or median. It defines absolute and relative measures of dispersion and explains key measures like range, interquartile range, quartile deviation, mean deviation, and their coefficients. Examples are provided to demonstrate calculating each measure for both ungrouped and grouped data. The advantages and disadvantages of range, quartile deviation, and mean deviation are also outlined.

K Nearest neighbour

This document discusses nearest neighbor classification, which is an instance-based machine learning algorithm. It stores all training records and classifies new records based on their similarity to stored examples, by finding the k nearest neighbors and predicting the class as the most common label among those neighbors. Key aspects covered include using a distance metric like Euclidean distance to find neighbors, choosing an optimal k value, addressing scaling issues between attributes, and challenges with high-dimensional data.

Knn

K-Nearest Neighbor (KNN) is a supervised machine learning algorithm that can be used for classification and prediction. It finds the K closest training examples to a new data point and assigns the most common class among those K examples to the new data point. Euclidean distance is often used to calculate the distance between points. An example is provided of classifying a new paper sample as good or bad based on acid durability and strength attributes by finding its 3 nearest neighbors and assigning it the majority class.

TunUp final presentation

This document summarizes a distributed cloud-based genetic algorithm framework called TunUp for tuning the parameters of data clustering algorithms. TunUp integrates existing machine learning libraries and implements genetic algorithm techniques to tune parameters like K (number of clusters) and distance measures for K-means clustering. It evaluates internal clustering quality metrics on sample datasets and tunes parameters to optimize a chosen metric like AIC. The document outlines TunUp's features, describes how it implements genetic algorithms and parallelization, and concludes it is an open solution for clustering algorithm evaluation, validation and tuning.

Fa18_P2.pptx

This document discusses density-based clustering algorithms. It begins by outlining the limitations of k-means clustering, such as its inability to find non-convex clusters or determine the intrinsic number of clusters. It then introduces DBSCAN, a density-based algorithm that can identify clusters of arbitrary shapes and handle noise. The key definitions and algorithm of DBSCAN are described. While effective, DBSCAN relies on parameter selection and cannot handle varying densities well. OPTICS is then presented as an augmentation of DBSCAN that produces a reachability plot to provide insight into the underlying cluster structure and avoid specifying the cluster count.

Part 2

The document discusses DBSCAN, a density-based clustering algorithm. DBSCAN identifies core points that have many nearby points and clusters are formed based on core point neighborhoods. It defines core, border and noise points based on minimum point and epsilon parameters. The time complexity of DBSCAN is O(n^2) in the worst case. Internal validation measures like sum of squared error (SSE) and external measures like entropy can evaluate clustering results. The similarity matrix approach also allows visual validation of clusters.

Variability

The document discusses variability and measures of variability. It defines variability as a quantitative measure of how spread out or clustered scores are in a distribution. The standard deviation is introduced as the most commonly used measure of variability, as it takes into account all scores in the distribution and provides the average distance of scores from the mean. Properties of the standard deviation are examined, such as how it does not change when a constant is added to all scores but does change when all scores are multiplied by a constant.

k-Nearest Neighbors with brief explanation.pptx

Completely explained about knn algorithm I'm ml

introduction to machine learning 3c-feature-extraction.pptx

This document discusses feature extraction and dimensionality reduction techniques. It begins by defining feature extraction as mapping a set of features to a reduced feature set that maximizes classification ability. It then explains principal component analysis (PCA) and how it works by finding orthogonal directions that maximize data variance. However, PCA does not consider class information. Linear discriminant analysis (LDA) is then introduced as a technique that finds projections by maximizing between-class distance and minimizing within-class distance to better separate classes. LDA thus provides a "good projection" for classification tasks.

Data Mining Lecture_5.pptx

Lecture 5: Similarity and Distance. Metrics. Min-wise independent hashing. (ppt,pdf)
Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.
Chapter 2 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.

similarities-knn.pptx

Installing and configuring computer systems involves competencies to assemble computer hardware, install operating systems and drivers for peripherals/devices, and install application software as well as to conduct testing and documentation.
This learning activity sheet will help you understand better the concepts and skills of installing and configuring computer systems. Try your best to go over the activities and answer the questions or items asked for.
Learning

KNN presentation.pdf

KNN presentation.pdf

Knn

Knn

Knn

Knn

Classification_Algorithms_Student_Data_Presentation

Classification_Algorithms_Student_Data_Presentation

07 learning

07 learning

Clasification approaches

Clasification approaches

knn-1.pptx

knn-1.pptx

Knn

Knn

DBSCAN (2014_11_25 06_21_12 UTC)

DBSCAN (2014_11_25 06_21_12 UTC)

Measures of Dispersion.pptx

Measures of Dispersion.pptx

K Nearest neighbour

K Nearest neighbour

Knn

Knn

TunUp final presentation

TunUp final presentation

Fa18_P2.pptx

Fa18_P2.pptx

Part 2

Part 2

Variability

Variability

k-Nearest Neighbors with brief explanation.pptx

k-Nearest Neighbors with brief explanation.pptx

introduction to machine learning 3c-feature-extraction.pptx

introduction to machine learning 3c-feature-extraction.pptx

Data Mining Lecture_5.pptx

Data Mining Lecture_5.pptx

similarities-knn.pptx

similarities-knn.pptx

132/33KV substation case study Presentation

132/33Kv substation case study ppt

Digital Twins Computer Networking Paper Presentation.pptx

A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.

学校原版美国波士顿大学毕业证学历学位证书原版一模一样

原版一模一样【微信：741003700 】【美国波士顿大学毕业证学历学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...

Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.

Embedded machine learning-based road conditions and driving behavior monitoring

Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.

Software Engineering and Project Management - Software Testing + Agile Method...

Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.

一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理

原版一模一样【微信：741003700 】【(爱大毕业证书)爱荷华大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

原版一模一样【微信：741003700 】【(uofo毕业证书)美国俄勒冈大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

Applications of artificial Intelligence in Mechanical Engineering.pdf

Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.

AI for Legal Research with applications, tools

AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.

Comparative analysis between traditional aquaponics and reconstructed aquapon...

The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.

Engineering Standards Wiring methods.pdf

Engineering Standards Wiring methods.pdf

Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...

This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.

2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf

2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building

Advanced control scheme of doubly fed induction generator for wind turbine us...

This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.

Software Engineering and Project Management - Introduction, Modeling Concepts...

Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.

一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理

原版一模一样【微信：741003700 】【(osu毕业证书)美国俄勒冈州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

132/33KV substation case study Presentation

132/33KV substation case study Presentation

Digital Twins Computer Networking Paper Presentation.pptx

Digital Twins Computer Networking Paper Presentation.pptx

学校原版美国波士顿大学毕业证学历学位证书原版一模一样

学校原版美国波士顿大学毕业证学历学位证书原版一模一样

VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...

VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...

Embedded machine learning-based road conditions and driving behavior monitoring

Embedded machine learning-based road conditions and driving behavior monitoring

Software Engineering and Project Management - Software Testing + Agile Method...

Software Engineering and Project Management - Software Testing + Agile Method...

一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理

一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

Applications of artificial Intelligence in Mechanical Engineering.pdf

Applications of artificial Intelligence in Mechanical Engineering.pdf

AI for Legal Research with applications, tools

AI for Legal Research with applications, tools

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Comparative analysis between traditional aquaponics and reconstructed aquapon...

Comparative analysis between traditional aquaponics and reconstructed aquapon...

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

Engineering Standards Wiring methods.pdf

Engineering Standards Wiring methods.pdf

Properties Railway Sleepers and Test.pptx

Properties Railway Sleepers and Test.pptx

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- 1. Jaskaran Singh and Vansh Algorithms: K Nearest Neighbors 1
- 2. Simple Analogy.. • Tell me about your friends(who your neighbors are) and I will tell you who you are. 2
- 3. Instance-based Learning Its very similar to a Desktop!! 3
- 4. KNN – Different names • K-Nearest Neighbors • Memory-Based Reasoning • Example-Based Reasoning • Instance-Based Learning • Lazy Learning 4
- 5. What is KNN? • A powerful classification algorithm used in pattern recognition. • K nearest neighbors stores all available cases and classifies new cases based on a similarity measure(e.g distance function) • One of the top data mining algorithms used today. • A non-parametric lazy learning algorithm (An Instance- based Learning method). 5
- 6. KNN: Classification Approach • An object (a new instance) is classified by a majority votes for its neighbor classes. • The object is assigned to the most common class amongst its K nearest neighbors.(measured by a distant function ) 6
- 7. 7
- 8. Distance Measure Training Records Test Record Compute Distance Choose k of the “nearest” records 8
- 9. Distance measure for Continuous Variables 9
- 10. Distance Between Neighbors • Calculate the distance between new example (E) and all examples in the training set. • Euclidean distance between two examples. – X = [x1,x2,x3,..,xn] – Y = [y1,y2,y3,...,yn] – The Euclidean distance between X and Y is defined as: 10 n i i i y x Y X D 1 2 ) ( ) , (
- 11. K-Nearest Neighbor Algorithm • All the instances correspond to points in an n-dimensional feature space. • Each instance is represented with a set of numerical attributes. • Each of the training data consists of a set of vectors and a class label associated with each vector. • Classification is done by comparing feature vectors of different K nearest points. • Select the K-nearest examples to E in the training set. • Assign E to the most common class among its K-nearest neighbors. 11
- 12. 3-KNN: Example(1) Distance from John sqrt [(35-37)2+(35-50)2 +(3- 2)2]=15.16 sqrt [(22-37)2+(50-50)2 +(2- 2)2]=15 sqrt [(63-37)2+(200-50)2 +(1- 2)2]=152.23 sqrt [(59-37)2+(170-50)2 +(1- 2)2]=122 sqrt [(25-37)2+(40-50)2 +(4- 2)2]=15.74 12 Customer Age Income No. credit cards Class George 35 35K 3 No Rachel 22 50K 2 Yes Steve 63 200K 1 No Tom 59 170K 1 No Anne 25 40K 4 Yes John 37 50K 2 ? YES
- 13. How to choose K? • If K is too small it is sensitive to noise points. • Larger K works well. But too large K may include majority points from other classes. • Rule of thumb is K < sqrt(n), n is number of examples. 13 X
- 14. 14
- 15. X X X (a) 1-nearest neighbor (b) 2-nearest neighbor (c) 3-nearest neighbor K-nearest neighbors of a record x are data points that have the k smallest distance to x 15
- 16. KNN Feature Weighting • Scale each feature by its importance for classification • Can use our prior knowledge about which features are more important • Can learn the weights wk using cross‐validation (to be covered later) 16
- 17. Feature Normalization • Distance between neighbors could be dominated by some attributes with relatively large numbers. e.g., income of customers in our previous example. • Arises when two features are in different scales. • Important to normalize those features. – Mapping values to numbers between 0 – 1. 17
- 18. Nominal/Categorical Data • Distance works naturally with numerical attributes. • Binary value categorical data attributes can be regarded as 1 or 0. 18
- 19. KNN Classification $0 $50,000 $100,000 $150,000 $200,000 $250,000 0 10 20 30 40 50 60 70 Non-Default Default Age Loan$ 19
- 20. KNN Classification – Distance Age Loan Default Distance 25 $40,000 N 102000 35 $60,000 N 82000 45 $80,000 N 62000 20 $20,000 N 122000 35 $120,000 N 22000 52 $18,000 N 124000 23 $95,000 Y 47000 40 $62,000 Y 80000 60 $100,000 Y 42000 48 $220,000 Y 78000 33 $150,000 Y 8000 48 $142,000 ? 2 2 1 2 2 1 ) ( ) ( y y x x D 20
- 21. KNN Classification – Standardized Distance Age Loan Default Distance 0.125 0.11 N 0.7652 0.375 0.21 N 0.5200 0.625 0.31 N 0.3160 0 0.01 N 0.9245 0.375 0.50 N 0.3428 0.8 0.00 N 0.6220 0.075 0.38 Y 0.6669 0.5 0.22 Y 0.4437 1 0.41 Y 0.3650 0.7 1.00 Y 0.3861 0.325 0.65 Y 0.3771 0.7 0.61 ? Min Max Min X Xs 21
- 22. Strengths of KNN • Very simple and intuitive. • Can be applied to the data from any distribution. • Good classification if the number of samples is large enough. 22 Weaknesses of KNN • Takes more time to classify a new example. • need to calculate and compare distance from new example to all other examples. • Choosing k may be tricky. • Need large number of samples for accuracy.

- Based on similarity function “tell me who your neighbors are, and I’ll tell you who you are” Simplest Algorithms
- KNN stattistical estimation Pattern recognition in the beginning of 1970
- Algorithm
- Weight and hight of people and we assume Red : female Blue: Male So we have new measurement of hight and weight … K=1,female ,we assigned class =female K=5 3 female, 2 males K=odd K=6 we have ties
- Although there are other possible choices, most instance-based learners use Euclid- ean distance.
- Although there are other possible choices, most instance-based learners use Euclid- ean distance. Other distance metrics may be more appropriate in special circumstances. If we have too many points Sum the squared differences and we get the square roots In Manhanttan distances : get absoulte values Min-ko-waski distance= Different distance measure .. We use the common one
- N-feature size
- This single number K is given prior to the testing phase and this number decides how many neighbors influence the classification.
- If the sample set is finite.. Historically the optomal K for mpst datasets has been between 3-10 . That produces much better results than 1NN
- Some configurations over data before applying KNN Different attributes are often measured on different scales, Numerical: Here the difference between two values is just the numerical difference between them, and it is this difference that is squared and added to yield the distance functio Nominal: the difference between two values that are not the same is often taken to be 1, whereas if the values are the same the difference is 0. No scaling is required in this case because only the values 0 and 1 are used.
- For attributes with more than two values, binary indicator variables are used as an implicit solution. Ex: In a customers’ Bank field with 3 values, Bank1 – 100 Bank2 – 010 Bank3 - 001
- That’s price to pay
- Robust: strong and healthy; vigorou