Lecture 6

What is kNN Classification?

kNN, or the k-nearest neighbor algorithm, is a machine learning algorithm that uses proximity to compare one data point with a set of data it was trained on and has memorized to make predictions. This instance-based learning affords kNN the 'lazy learning' denomination and enables the algorithm to perform classification or regression problems. kNN works off the assumption that similar points can be found near one another — birds of a feather flock together.

As a classification algorithm, kNN assigns a new data point to the majority set within its neighbors. As a regression algorithm, kNN makes a prediction based on the average of the values closest to the query point.

What are Classification and Regression Trees

Classification and regression trees: Classification and regression trees are methods used in machine learning to create models that can be used to make predictions about data. Classification trees are used to predict categorical data, such as whether an email is spam or not, while regression trees are used to predict numerical data, such as the price of a stock.

Classification and regression trees are powerful tools for analysing data. They can provide valuable insights into how to better understand complex datasets and help us make decisions about our future actions. But what exactly are classification and regression trees? How do they work, and why should we care about them? This article will explain the fundamentals of this important tool, detailing its benefits and limitations in order to give readers an understanding of how it works and how it can be used most effectively.

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How to Find the Optimal Value of k in kNN?KNN is a widely used machine learning algorithm in supervised learning tasks. It is known as k-Nearest Neighbors. It can be used for both regression and classification problems. It is a non-parametric learning algorithm. KNN is relatively simple and easy to understand, making it a popular choice, especially for beginners in machine learning. However, it can be sensitive to the choice of k and the distance metric used, requiring careful tuning for optimal performance.

One of the critical aspects of implementing KNN effectively is determining the optimal value of k, the number of nearest neighbors considered for predictions. This article will walk you through the process of finding the optimal k in KNN, discussing various techniques, approaches, model implementation, applications, advantages and disadvantages of KNN .

Decision Trees for Decision Making

The management of a company that I shall call Stygian Chemical Industries, Ltd., must decide whether to build a small plant or a large one to manufacture a new product with an expected market life of 10 years. The decision hinges on what size the market for the product will be.

Possibly demand will be high during the initial two years but, if many initial users find the product unsatisfactory, will fall to a low level thereafter. Or high initial demand might indicate the possibility of a sustained high-volume market. If demand is high and the company does not expand within the first two years, competitive products will surely be introduced.

Overfitting vs. Underfitting: Finding the BalanceWhen data scientists and engineers train machine learning (ML) models, they risk using an algorithm that is too simple to capture the underlying patterns in the data, leading to underfitting, or one that is too complex, leading to overfitting. Managing overfitting and underfitting is a core challenge in data science workflows and developing reliable artificial intelligence (AI) systems. Bias and variance explain the balance engineers need to strike to help ensure a good fit in their machine learning models. As such, the bias-variance tradeoff is central to addressing underfitting and overfitting.
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Discovering the Optimal Ratio for Data Splitting

It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should be used for training and testing. In this article, we show that the optimal training/testing splitting ratio is sqrt p:1, where p is the number of parameters in a linear regression model that explains the data well.

Data splitting is a commonly used approach for model validation, where we split a given dataset into two disjoint sets: training and testing. The statistical and machine learning models are then fitted on the training set and validated using the testing set. By holding out a set of data for validation separate from training, we can evaluate and compare the predictive performance of different models without worrying about possible overfitting on the training set.
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