k-NN Classification Quiz

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Questions and Answers

What is the primary reason for selecting an odd value of k in the k-NN algorithm?

To create a balanced classification outcome

To increase the number of neighbors considered

To ensure computational efficiency

To avoid ties between classes during classification (correct)

In a k-NN classification scenario, what happens when a test data-point has an equal number of neighbors from two different classes?

The point is assigned a label based on distance to the neighbors

A tie-breaker algorithm automatically selects a class

The data-point cannot be classified without uncertainty (correct)

The test data-point is classified based on arbitrary choice

When applying the k-NN algorithm, what should be the focus for classifying a data-point effectively?

Only the nearest training data-points need to be analyzed (correct)

Consideration of all training data-points equally

Weights should be assigned based on distance to neighbors

Only the furthest training data-points should influence classification

What can lead to classification uncertainty in the k-NN algorithm?

Selecting an even k value Signup and view all the answers

What characterizes the predicted label for a test data point when using the k-NN algorithm?

It is the most common label among the nearest neighbors Signup and view all the answers

What is the main purpose of the Gaussian Naive Bayes algorithm?

To classify data points based on probability distributions. Signup and view all the answers

In the context of a linear classifier, what does the sign of the dot-product with the weight vector indicate?

The relative angle between the data point and the weight vector. Signup and view all the answers

When using the K-NN algorithm to classify a new data point, how many distance calculations are required for a dataset of n data points?

All n distances need to be computed. Signup and view all the answers

In a binary classification problem, if the weight vector indicates a negative dot-product for a data point, what label will it likely be assigned?

Negative class label. Signup and view all the answers

What is a key assumption made by Gaussian Naive Bayes regarding the features?

Features are independent given the class label. Signup and view all the answers

What defines the decision boundary in a linear classifier?

The hyperplane formed by the weight vector. Signup and view all the answers

Which of the following is NOT typically a characteristic of Gaussian distribution in classification?

All features must have the same variance. Signup and view all the answers

What is the primary output of applying parameter estimation in Naive Bayes?

Probability estimates for each class. Signup and view all the answers

What is the primary purpose of using parameter estimation in a Naive Bayes algorithm?

To estimate probabilities for classification Signup and view all the answers

Given a binary classification scenario with estimated values of 0.3 for one label, what is the probable threshold for predicting the opposite label?

0.6 Signup and view all the answers

In a k-NN algorithm, what does the 'k' represent?

The number of neighbors to consider for voting Signup and view all the answers

What conclusion can be drawn about the storage requirements of the k-NN algorithm?

It must save the entire dataset for accurate predictions Signup and view all the answers

If a test point is classified without uncertainty as 'red' by a k-NN classifier, what can be concluded about the nearest neighbors?

The nearest neighbors are overwhelmingly of the 'red' class Signup and view all the answers

What is a common misconception regarding the Gaussian distribution in the context of Naive Bayes?

It cannot handle categorical data effectively Signup and view all the answers

When estimating parameters for a feature with three possible values, how many distinct parameters are typically necessary in a Naive Bayes model?

Two parameters Signup and view all the answers

In a binary classification task with 1000 data points, what role does the probability estimate of 0.3 play in deciding the predicted label?

It indicates the likelihood of the label being '0' Signup and view all the answers

Study Notes

Graded Assignment Study Notes

Question 1: Consider two generative model-based algorithms (Model 1 and Model 2). Model 1's feature occurrences aren't independent of each other, while Model 2 assumes conditional independence of features from the label. Model 1 requires estimating more independent parameters.
Question 2: The correct statement(s) about the Naive Bayes algorithm for binary classification with all binary features is (are) that if the probability p is 0 for a label, then p is also 0 for the other label within the same parameter and there are no labeled instances in the training set with a j-th feature value as 1 for a specific label Y.
Question 3: For a Naive Bayes model trained on a dataset containing d features with labels 0 and 1, the expression P(y = 1|x) > P(y = 0|x) is sufficient for predicting the label as 1.
Question 4: Consider a binary classification dataset that contains only one feature. The data points for label 0 follow a normal distribution with mean 0 and a variance of 4 and for label 1 follow a normal distribution with mean 2. The variance is σ². If the decision boundary learned using a Gaussian Naive Bayes algorithm is linear, the value of σ² is 2
Question 5: Consider a binary classification dataset with two binary features (f1 and f2). If f2 is always 0 for label 0 examples and can be 1 or 0 for label 1 examples, insufficient information is present to predict for [1,1] using a Naive Bayes algorithm.
Question 6 and 7: Consider binary classification with two features f₁ and f₂ that follow a Gaussian distribution. The values of p (estimate for P(y=1)) in question 6 is 0.5. In question 7, p is not stated, but the model involves estimating parameters for the distributions of f given the label y.
Question 8: Consider a binary classification dataset (f1 and f2). f₁ is categorical, taking three values, and f₂ is numerical with Gaussian distribution. Using Naive Bayes, 9 independent parameters are needed to be estimated.
Question 9: A naive Bayes algorithm was run on a binary classification. The probability of label y=1 is 0.3. The probabilities concerning features f1 = 1 | y = 0 is 0.2, f2= 1 | y = 0 is 0.3, f1 = 1 | y = 1 is 0.1 and f2= 1 | y = 1 is 0.2. The probability of feature value f2 = 0 | y=1 is 0.8.
Question 10 and 11: Consider a binary classification dataset (f1 and f2) with all binary features. Question 10 asks for the value of P(f2 = 0 | y = 1), given certain data. Question 11 is about classifying a data point [0,1].
Question 1: Consider a classification problem using a K-NN algorithm with a dataset of 1000 points. Statement S1 (k=10, it's enough to store 10 points) and S3 (more data points need to be stored with a higher k value) are correct. Statement S2 (need the entire dataset when k=10) and S4 are correct, where S4 has no dependency on k
Question 2: Consider a binary classification dataset and a k-NN classifier with k = 4. The black train point should be colored red; if it were blue, there is uncertainty.
Question 3: Consider a k-NN algorithm with k = 3. The predicted label for the test point given by the provided data is 1.
Question 4: A decision tree is terminated at the given level. The labels for L1 and L2 are 0 and 1 respectively.
Question 5: If Entropy is used to measure impurity and p is the proportion belonging to class-1, then the impurity of L1 is 0.954
Question 6: Information Gain is 0.030
Question 7: A test point passes through a minimum of 1 and a maximum of 4 questions before being assigned a label.
Question 8: The impurity of a node p increases from 0 to 0.5. It decreases afterwards. The value p = 0.5 corresponds to the case of maximum impurity.
Question 9: The weight vector for the given dataset is [1 1 -1 -1]
Question 10: The valid decision regions for a decision tree are horizontal lines and vertical lines.
Question 11: What is the value of P(y = 1)?
Question 12: The predicted label for a test point [0, 1]T is 0.
Question 1: Model 1 has more independent parameters.
Question 2: The correct answer is "Insufficient information to predict".
Question 1: The correct answer is (c) because S1 and S3 are true statements.
Question 2: The correct answer is (b).
Question 3: The correct answer is (c).
Question 4: The correct answer is (d).
Question 5: The correct answer is 0.98.
Question 6: The correct answer is 0.030.
Question 7: The correct answer is max = 4.
Question 8: The correct answer is (d).
Question 9: The correct answer is (b).
Question 10: The correct answer is (a).
Question 1-10, Practice: A variety of questions addressing different aspects of machine learning, including classification, regression, cross-validation, and model selection. Several require identifying correct statements or selecting the best model among different choices based on given data, characteristics, or performance metrics.

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Description

Test your knowledge on the k-NN classification algorithm with this quiz. Explore the reasons for selecting an odd value of k, handling equal neighbors from different classes, and the factors that lead to classification uncertainty. These questions will help solidify your understanding of k-NN.