# Which of the following statements is true for knn classifiers

## KNN Basics

KNN is a non-parametric and lazy learning algorithm. Non-parametric means there is no assumption for underlying data distribution. A lazy algorithm means it does not need any training data points for model generation. It works on similarity measures to predict the class of data points. It is also called an instance-based and memory-based algorithm.

### KNN is a non-parametric method

KNN is a non-parametric method, meaning it does not make any assumptions about the underlying data. One of the main advantages of using KNN is that it can be used for data that is not linearly separable.

### KNN is a supervised learning algorithm

KNN is a supervised learning algorithm, which means that it can be used for both regression (predicting a continuous value) and classification (predicting a class label).

### KNN can be used for both classification and regression

KNN can be used for both classification and regression. However, it is more commonly used for classification.

## KNN Algorithm

KNN is a supervised learning algorithm that can be used for both classification and regression problems. The aim of this algorithm is to find the k nearest neighbors of a given data point and predict the class label based on the majority class of the neighbors.

### KNN works by finding the nearest neighbors of a given data point

KNN works by finding the nearest neighbors of a given data point. It then uses those neighbors to make predictions about the point’s label.

### KNN can be used for both linear and non-linear data

KNN can be used for both linear and non-linear data. Linear data is data that can be described by a straight line, while non-linear data is data that cannot be described by a straight line. KNN is a non-parametric algorithm, which means that it does not make any assumptions about the underlying data. This makes it well suited for data that is not well suited for other kinds of models, such as linear models.

## KNN Applications

KNN classifiers are used in a variety of applications such as pattern recognition, data compression, and machine learning. KNN classifiers are also used in drug discovery, stock market prediction, and intrusion detection.

### KNN can be used for both classification and regression

KNN can be used for both classification and regression. However, it is more commonly used for classification. In a classification problem, the goal is to predict the class label of a new data point. For example, you could use a KNN classifier to predict whether a new customer will purchase a product or not. In a regression problem, the goal is to predict the value of a new data point. For example, you could use a KNN regressor to predict the price of a new house.

### KNN can be used for both linear and non-linear data

KNN can be used for both linear and non-linear data. If the data is linearly separable, then KNN will always find the correct classification. However, if the data is not linearly separable, then KNN may not find the correct classification.

KNN is a powerful tool for classification tasks when the data is not linearly separable. KNN can also be used for regression tasks. KNN is easy to understand and implement. KNN is also a non-parametric model, which means that it does not make any assumptions about the data.

### KNN is a simple algorithm

One of the main advantages of KNN is that it is a simple algorithm to understand and implement. It is also an instance-based learning algorithm, meaning that it does not require any training prior to making predictions. This can be a disadvantage if there are a lot of training data points, but is an advantage if the training data is limited or noisy. Another advantage of KNN is that it is flexible and can be used for classification or regression tasks.

### KNN is easy to implement

KNN is considered to be a lazy learning algorithm. This is because it does not make any assumptions on the data and just stores the training data. It is also easy to implement as there are only a few parameters that need to be set. The advantage of using KNN is that it can be used for both classification and regression problems.

### KNN is versatile and can be used for both classification and regression

One of the most powerful aspects of KNN is its versatility. It can be used for both classification and regression problems. This is a big advantage over other techniques like decision trees and logistic regression, which can only be used for one or the other.

KNN is also fairly intuitive and easy to explain. The algorithm is based on a very simple concept: similarity. This makes it easy to understand how the algorithm works and why it works well.

Another advantage of KNN is that it’s non-parametric, meaning it doesn’t make any assumption about the underlying data distribution. This is a good thing because, in many real-world cases, the data may not be Normally distributed (or any other parametric distribution).

Finally, KNN is very simple to implement and there are few parameters that need to be tuned. You can pretty much use the default values for all the parameters and still get good results.