Applied Data Science - Lecture 5: k-NN
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Applied Data Science - Lecture 5: k-NN

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What is the main purpose of calculating distances in the k-NN algorithm?

  • To determine the proximity of the test point to the training data (correct)
  • To create clusters of similar points
  • To reduce the dimensionality of the dataset
  • To classify points based on a predefined threshold

    • Which of the following statements is true about weights in the k-NN algorithm?

  • Weights are assigned based on the square of the distance
  • Weights are calculated only for the farthest neighbor
  • All nearest neighbors contribute equally regardless of distance
  • Weights can be the inverse of the distance (correct)

    • In the classification result, why is point T classified as Class 1?

  • It has the highest total weight from its neighbors (correct)
  • It is the mean of the classes
  • It is a majority in the training data
  • It is nearest to Class 2 points

    • What distance measure is primarily used in the calculation of k-NN?

    <p>Euclidean distance</p> Signup and view all the answers

    What is the maximum number of neighbors that can influence the classification if k=3?

    <p>3</p> Signup and view all the answers

    What does the 'k' in k-NN represent?

    <p>The number of nearest neighbors considered for classification</p> Signup and view all the answers

    In a k-NN algorithm, what is a possible consequence when data points are located between class boundaries?

    <p>Classification may become ambiguous due to multiple species in proximity.</p> Signup and view all the answers

    How are points represented in the context of the k-NN algorithm?

    <p>As points in a multi-dimensional space based on attributes</p> Signup and view all the answers

    What role does the Iris dataset play when analyzing the k-NN algorithm?

    <p>It provides a visual representation of predictions based on feature values.</p> Signup and view all the answers

    Which approach is used to determine the nearest training data point in the k-NN algorithm?

    <p>Finding the nearest point in multi-dimensional space</p> Signup and view all the answers

    Study Notes

    Applied Data Science - DS403

    • Course taught by Dr. Nermeen Ghazy
    • Reference books mentioned:
      • Applied Data Science by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger
      • Data Science: Concepts and Practice by Vijay Kotu and Bala Deshpande

    Lecture 5 - K Nearest Neighbors (k-NN)

    • Similar data points cluster together in multi-dimensional space, sharing the same target class labels. This is the core concept of k-NN.
    • K-NN is a classification algorithm.

    How k-NN Works

    • Each data point can be visualized as a point in an n-dimensional space (n is the number of attributes).
    • High-dimensional spaces are difficult to visualize, but the mathematical operations are still applicable.
    • The algorithm identifies the "k" nearest points to a new, unclassified data point.
    • The class label of the new point is determined by a majority vote among these "k" nearest neighbors. When k =1, the single nearest neighbor is used.

    Calculating Distances

    • Euclidean distance is a common method to measure the proximity between two points in multi-dimensional space.
    • Distance formula: d = √((x1 - y1)² + (x2 - y2)² +...+ (xn - yn)²)
    • This formula extends to higher dimensions with an additional attribute added to the sum.

    Weights in k-NN

    • Weighted voting is used in some implementations of k-NN. Weights are proportional to the inverse of the distance from the test point.
    • Points closer to the test point have higher weights.
    • All weights add up to one.

    Implementing k-NN

    • Common spreadsheet tools, such as MS Excel, have functions for referencing datasets.
    • Tools like RapidMiner use data preparation, modeling, and performance evaluation in a consistent process and have a specific k-NN operator.

    Data Preparation for k-NN

    • The dataset for Iris example has 150 examples and 4 numeric attributes.
    • Data normalization is often performed to transform numeric attributes.
    • Data is split into training and testing sets.

    k-NN Parameters

    • The parameter 'k' , which refers to the number of nearest neighbors to consider for classification, can be configured. Common settings in datasets use 3, or 1
    • Weights are often used in the voting step and are calculated considering distance.
    • Numerous distance measures are available. The measure choice affects model input types.

    Execution and Interpretation of k-NN

    • The k-NN result, or training data set, consists of the already classified records. No additional information is typically provided other than the training data statistics.
    • Performance evaluation, typically a confusion matrix, for the test set shows the accuracy of the predictions.
    • The classification of individual test datapoints is also examined.

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    Description

    This quiz covers the concepts of K Nearest Neighbors (k-NN), a fundamental classification algorithm used in data science. It focuses on how k-NN works, including the visualization of data points in multi-dimensional space and the method of determining class labels through majority voting among the nearest neighbors. Test your understanding of this key data science technique!

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