KNN Classification: An Overview

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Questions and Answers

In classification, what is the purpose of constructing a model?

  • To predict future data volumes.
  • To optimize data retrieval speeds.
  • To analyze data storage efficiency.
  • To predict class labels (categories). (correct)

During the Classification step of the basic General Approach, what data is used?

  • Training set
  • Test set (correct)
  • Validation set
  • Empty set

Which of the following steps is crucial in the Learning (training) step when constructing a classification model?

  • Learning from a training dataset with associated classes. (correct)
  • Using only a subset of available features to simplify the model.
  • Ignoring data tuples with undefined classes.
  • Applying unsupervised learning techniques.

What is the purpose of estimating classifier accuracy and trying to avoid overfitting?

<p>To ensure the classifier generalizes well to new, unseen data. (C)</p>
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How does instance-based learning differ from learning methods that construct an explicit description of the target function?

<p>Instance-based learning constructs the target function only when a new instance must be classified. (C)</p>
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What is a key characteristic of instance-based learning methods?

<p>They examine the relationship of a new instance to previously stored examples. (C)</p>
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How do instance-based methods estimate the target function?

<p>They estimate the target function locally and differently for each new instance. (B)</p>
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What does learning consist of in instance-based algorithms?

<p>Storing the presented training data. (D)</p>
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What is a significant advantage of instance-based learning?

<p>Ability to learn complex target functions. (B)</p>
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One of the disadvantages of instance-based learning is:

<p>High cost of classifying new instances. (C)</p>
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What issue arises in instance-based learning when the target concept depends only on a few of the many available attributes?

<p>The truly 'similar' instances may be a large distance apart. (A)</p>
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Which of the following best describes the k-Nearest Neighbor (KNN) algorithm?

<p>A supervised machine learning algorithm useful for classification problems. (C)</p>
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What is the primary calculation involved in the k-NN classifier algorithm to make predictions?

<p>Calculating the distance between the test data and the input data. (D)</p>
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What is the 'Curse of Dimensionality' in the context of Nearest Neighbor algorithms?

<p>A situation where the 'neighborhood' becomes very large in high-dimensional spaces. (C)</p>
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In k-NN, how does the presence of irrelevant features affect the algorithm's performance?

<p>Irrelevant features can distort distance calculations, reducing accuracy. (C)</p>
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What does the k-NN algorithm identify given N training vectors?

<p>The k nearest neighbors of a query point, regardless of labels. (A)</p>
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In the context of the k-NN algorithm, what is 'parameter tuning'?

<p>The process of choosing the optimal value of 'k' for better accuracy. (A)</p>
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When k = 1 in k-NN, what does each training vector define in space?

<p>A Voronoi partition of the space. (A)</p>
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What is the first step in the k-NN classifier algorithm?

<p>Choose the number K of neighbors. (D)</p>
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For a 2-class problem, what is a common recommendation for choosing the value of 'k' in k-NN?

<p>Choose an odd 'k' value to avoid ties in voting. (D)</p>
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Why should 'k' not be a multiple of the number of classes?

<p>To avoid ties when determining the majority class (C)</p>
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What is a main drawback of k-NN regarding its computational complexity?

<p>The complexity in searching the nearest neighbors for each sample. (D)</p>
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Choosing a small value for K in k-NN typically leads to:

<p>Low bias and high variance (Overfitting). (C)</p>
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Choosing a large value for K in k-NN typically leads to:

<p>High bias and low variance (Underfitting). (A)</p>
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How is the value of k determined in k-NN?

<p>Square root of number of data points (B)</p>
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When is generally a good scenerio to use KNN:

<p>When data is smaller and labelled (D)</p>
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Given a dataset of individuals classified as 'Normal' or 'Underweight' based on height and weight, what distance calculation is typically used to determine the nearest neighbors for a new individual?

<p>Euclidean distance (B)</p>
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Restaurant A sells burgers with optional flavors: Pepper, Ginger, and Chilly. How do you calculate the distance between the categorical values of two instances for Hamming distance?

<p>If the categorical values are same or matching, the distance is 0, otherwise 1. (C)</p>
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Restaurant A sells burgers with optional flavors: Pepper, Ginger, and Chilly. If value of Pepper is blue and Ginger is red then what is the hamming distance?

<p>1 (D)</p>
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Consider a KNN algorithm setup given pepper: false, ginger: true, chilly: true. Use Hamming Distance and find the distance from Query Example to Restaurant option A with pepper: True, ginger: True, chilly: True.

<p>1 (D)</p>
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Consider a KNN algorithm setup given pepper: false, ginger: true, chilly: true. Use Hamming Distance and find the distance from Query Example to Restaurant option A with pepper: True, ginger: True, chilly: True, then select 3NN algorithm. What is the correct KNN?

<p>2 (C)</p>
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What is the first step while using KNN to determining if a test patient is diabetic, given examples of BMI, Age, and Sugar?

<p>Calculating the distance between the test patient and training. (A)</p>
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If there are 10 examples in a dataset with 2 classes, what value of k would you pick?

<p>3 (D)</p>
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Suppose we have computed the Euclidean distance of unknown point (57, 170) from all possible datapoints and at k=3 majority vote would classify this point as Normal. What do you infer from this?

<p>The class of (57, 170) can possibly be Normal (C)</p>
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Suppose in a Restaurant the optional flavors for burgers are pepper, ginger and chilly. Further suppose that we have kept a record of number of burgers that are liked each day. To determine if pepper: false, ginger: true and chilly :true should be recommend using 3-NN algorithm, which method should we use?

<p>Hamming Distance (B)</p>
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What distance parameters are needed to estimate a class using solved KNN.

<p>Nearest Sample Distance (D)</p>
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What are the steps required to estimate solved KNN except which ones?

<p>Estimate Voronoi points (B)</p>
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Flashcards

What is Classification?

A data analysis task where a model is constructed to predict class labels (categories).

The Basics General Approach

A two-step process: Learning (training) and Classification (testing).

Learning (training) step

Construct a classification model from a training dataset.

Classification step

Use the model to predict class labels for given data (test set).

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Instance-based Learning

Constructs the target function only when classifying a new instance.

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Learning in Instance-based Algorithms

Storing the presented training data.

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Advantages of Instance-based learning

Training is very fast, learns complex target function, and doesn't lose information.

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"lazy" learning methods

delayed processing until a new instance must be classified.

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Disadvantages of Instance-based learning

High classifying cost and considers all attributes of the instances

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K-NN Classifier

A supervised machine learning algorithm useful for classification problems.

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K-Nearest Neighbor Classifier

Eucledian distance is used to determine which category a new data point belongs to?

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Curse of Dimensionality

Breaks down in high-dimensional spaces because the “neighborhood” often becomes very large.

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K-Nearest Neighbor Classifier

KNN Algorithm identifies the k nearest neighbors of 'c', regardless of labels.

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Choosing K

KNN Algorithm is based on feature similarity: Choosing the right value of k is a process called parameter tuning, and is important for better accuracy

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Voronoi partition of the space

Each training vector defines a region in space, defining a Voronoi partition of the space

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How to choose factor K

Odd value K is selected to avoid confusion between two classes of data

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Euclidean distance

Euclidean distance between two points in the plane with coordinates

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KNN

A ‘lazy learner' that doesn't learn a discriminative function from the training set

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Hamming distance

We can use Hamming distance to find the distance between the categorical values

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Study Notes

KNN Classification Overview

  • KNN stands for K-Nearest Neighbor and is used for classification tasks.

KNN in Data Analysis

  • Classification is a data analysis task that predicts class labels or categories using a constructed model.
  • KNN and other classifiers help predict the category a new data point belongs to based on existing data.

The Basics of Classification

  • Classification is a two-step process
  • The first step is the Learning (training) step. -This involves constructing a classification model by
    • building a classifier for a predetermined set of classes
    • learning from a training dataset that is comprised of data tuples and their associated classes, also considered supervised learning
  • The second step is the Classification step.
    • Here the model is used to predict class labels for given data or test set.

Instance-Based Learning

  • Instance-based learning contrasts with methods that create a general description of a target function from training examples; it constructs the target function only when a new instance is classified.
  • Each new query instance is evaluated against stored examples to assign a target function value.

K-NN Classifier Algorithm

  • k Nearest Neighbor (KNN) is a supervised machine learning algorithm useful for classification problems.
  • KNN works by calculating the distance between the test data and the input data, then making a prediction based on this calculation.

K-Nearest Neighbor Classifier in Practice

  • The Euclidean distance formula is commonly used, but other distance formulas can also be applied.
  • The choice of distance formula, number of neighbors, and model construction approach is up to the user.
  • Given N training vectors, the KNN algorithm identifies the k nearest neighbors.

How to Choose Factor K

  • KNN Algorithm is based on feature similarity.
  • Choosing the right value of k is a process called parameter tuning, is essential for better accuracy.
  • When k = 1, each vector defines a region in space called the Voronoi partition of the space

Steps for KNN Classifier Algorithm

  • Choose the number K of neighbors.
  • Take the K nearest neighbors of the new data point, according to the Euclidean distance.
  • Among these K neighbors, count the number of data points in each category.
  • Assign the new data point to the category where you counted the most neighbors.

Practical Advice for K-Nearest Neighbour

  • Use an odd value to avoid confusion
  • K chosen must not be a multiple of the number of classes.
  • A main drawback is the complexity in searching the nearest neighbors for each sample because of the complexity in searching the nearest neighbors.
  • Low value of k leads to low bias and high variance, and often overfitting.
  • High value of k leads to high bias and low variance, and often underfitting.
  • Optimal value offers a balance.

When to use KNN

  • KNN is useful when the dataset is small and labeled, and the data is noise free.
  • To find the nearest neighbors in KNN, calculate the Euclidean distance.

Solved examples

  • For a diabetic patient, K nearest neighbor classifier can be used to predict, assuming K is 3, given an input of BMI of 43.6 and age of 40.
  • To calculate distance for attributes with nominal or categorical values you can use Hamming distance to find the distance between values, where x1 and x2 are the attribute values of two instances. .
    • In hamming distance, If the categorical values are the same or matching, i.e. x1 is the same as x2, then distance is 0, otherwise 1.

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