# Do you need a training set for KNN?

Table of Contents

## Do you need a training set for KNN?

The model representation for KNN is the entire training dataset. It is as simple as that. KNN has no model other than storing the entire dataset, so there is no learning required.

**What is the training time of KNN classifier?**

So for KNN, the time complexity for Training is O(1) which means it is constant and O(n) for testing which means it depends on the number of test examples.

**Does KNN memorize the entire training set?**

KNN classifier does not have any specialized training phase as it uses all the training samples for classification and simply stores the results in memory.

### How do you train KNN?

Breaking it Down – Pseudo Code of KNN

- Calculate the distance between test data and each row of training data.
- Sort the calculated distances in ascending order based on distance values.
- Get top k rows from the sorted array.
- Get the most frequent class of these rows.
- Return the predicted class.

**How can I improve my KNN performance?**

Therefore rescaling features is one way that can be used to improve the performance of Distance-based algorithms such as KNN….The steps in rescaling features in KNN are as follows:

- Load the library.
- Load the dataset.
- Sneak Peak Data.
- Standard Scaling.
- Robust Scaling.
- Min-Max Scaling.
- Tuning Hyperparameters.

**What does training mean in KNN?**

During training phase, KNN arranges the data (sort of indexing process) in order to find the closest neighbors efficiently during the inference phase. Otherwise, it would have to compare each new case during inference with the whole dataset making it quite inefficient.

#### Is KNN NP hard?

kNN (k nearest neighbors) is one of the simplest ML algorithms, often taught as one of the first algorithms during introductory courses. It’s relatively simple but quite powerful, although rarely time is spent on understanding its computational complexity and practical issues.

**Is KNN flexible?**

We saw that this algorithm is very flexible because it does not assume that the data fits a specific model. However, it is also vulnerable to noise: If there are a few mislabeled points in the initial data set then new points near these will be misclassified. This can be thought of as a form of overfitting.

**Why KNN is called lazy learner?**

Why is the k-nearest neighbors algorithm called “lazy”? Because it does no training at all when you supply the training data. At training time, all it is doing is storing the complete data set but it does not do any calculations at this point.

## What happens when training a KNN model?

**How does KNN training work?**

KNN works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression).

**How do I overcome Overfitting in KNN?**

Solution: Smoothing. To prevent overfitting, we can smooth the decision boundary by K nearest neighbors instead of 1. Find the K training samples , r = 1 , … , K closest in distance to , and then classify using majority vote among the k neighbors.