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Instance-Based Learning Quiz Questions
1.
In instance-based learning, what is the purpose of instance reduction techniques?
A. To reduce the number of instances in the training data
B. To reduce the number of features in the training data
C. To reduce the complexity of the instance-based learning algorithm
D. To reduce the computation time during prediction
view answer:
A. To reduce the number of instances in the training data
Explanation:
The purpose of instance reduction techniques in instance-based learning is to reduce the number of instances in the training data, which can help reduce the computation time during prediction.
2.
What is the main limitation of k-Nearest Neighbors in handling imbalanced datasets?
A. The algorithm is biased towards the majority class
B. The algorithm is biased towards the minority class
C. The algorithm is not sensitive to class imbalance
D. The algorithm cannot handle imbalanced datasets
view answer:
A. The algorithm is biased towards the majority class
Explanation:
The main limitation of k-Nearest Neighbors in handling imbalanced datasets is that the algorithm is biased towards the majority class, as it is more likely to have majority class instances among the k nearest neighbors.
3.
What is an advantage of using a distance-weighted k-Nearest Neighbors algorithm?
A. It can handle missing values
B. It can handle imbalanced datasets
C. It reduces the effect of noisy instances
D. It increases the computation time during prediction
view answer:
C. It reduces the effect of noisy instances
Explanation:
Using a distance-weighted k-Nearest Neighbors algorithm reduces the effect of noisy instances, as instances closer to the query point have more influence on the classification decision.
4.
Which of the following is a lazy learning algorithm?
A. Decision Trees
B. k-Nearest Neighbors
C. Support Vector Machines
D. Neural Networks
view answer:
B. k-Nearest Neighbors
Explanation:
k-Nearest Neighbors is a lazy learning algorithm, as it does not create a model during the training phase and directly uses the training instances during prediction.
5.
Which of the following is NOT an issue in instance-based learning algorithms?
A. Curse of dimensionality
B. Handling missing values
C. Sensitivity to noisy instances
D. Sensitivity to irrelevant features
view answer:
D. Sensitivity to irrelevant features
Explanation:
Sensitivity to irrelevant features is not an issue in instance-based learning algorithms, as the similarity measures used can be adapted to ignore irrelevant features or feature selection techniques can be applied to remove irrelevant features before training.
6.
In the context of instance-based learning, what is an episodic memory?
A. A memory of specific instances in the training data
B. A memory of the general patterns in the training data
C. A memory of the similarity measures used during training
D. A memory of the classification rules learned during training
view answer:
A. A memory of specific instances in the training data
Explanation:
In the context of instance-based learning, an episodic memory is a memory of specific instances in the training data, which are used for classification during prediction.
7.
How can instance-based learning algorithms be adapted for anomaly detection tasks?
A. By setting a threshold on the distance to the k nearest neighbors
B. By setting a threshold on the number of nearest neighbors
C. By setting a threshold on the similarity measure
D. By setting a threshold on the classification accuracy
view answer:
A. By setting a threshold on the distance to the k nearest neighbors
Explanation:
Instance-based learning algorithms can be adapted for anomaly detection tasks by setting a threshold on the distance to the k nearest neighbors. Instances that have a large distance to their k nearest neighbors can be considered anomalies.
8.
What is the effect of noise on instance-based learning algorithms?
A. It reduces the performance of the algorithm
B. It has no effect on the performance of the algorithm
C. It improves the performance of the algorithm
D. It depends on the type of similarity measure used
view answer:
A. It reduces the performance of the algorithm
Explanation:
Noise generally reduces the performance of instance-based learning algorithms, as it can cause the algorithm to make incorrect classifications based on noisy instances.
9.
Which of the following instance-based learning algorithms is a non-linear classifier?
A. Linear Discriminant Analysis (LDA)
B. Logistic Regression
C. k-Nearest Neighbors
D. Perceptron
view answer:
C. k-Nearest Neighbors
Explanation:
k-Nearest Neighbors is a non-linear classifier, as it can capture complex non-linear patterns in the data without making any assumptions about the underlying data distribution.
10.
What is a key difference between the k-Nearest Neighbors algorithm and the k-Means algorithm?
A. k-Nearest Neighbors is an instance-based learning algorithm, while k-Means is a clustering algorithm.
B. k-Nearest Neighbors is a clustering algorithm, while k-Means is an instance-based learning algorithm.
C. k-Nearest Neighbors uses a distance metric, while k-Means uses a similarity measure.
D. k-Nearest Neighbors uses a similarity measure, while k-Means uses a distance metric.
view answer:
A. k-Nearest Neighbors is an instance-based learning algorithm, while k-Means is a clustering algorithm.
Explanation:
The key difference between the k-Nearest Neighbors algorithm and the k-Means algorithm is that k-Nearest Neighbors is an instance-based learning algorithm, while k-Means is a clustering algorithm.
11.
How can the performance of instance-based learning algorithms be improved when dealing with large datasets?
A. By using a more complex similarity measure
B. By using a simpler similarity measure
C. By using indexing structures to efficiently search for nearest neighbors
D. By using more nearest neighbors for classification
view answer:
C. By using indexing structures to efficiently search for nearest neighbors
Explanation:
The performance of instance-based learning algorithms can be improved when dealing with large datasets by using indexing structures (e.g., k-d trees, ball trees) to efficiently search for nearest neighbors, reducing the computation time during prediction.
12.
What is the effect of feature scaling on instance-based learning algorithms?
A. It improves the performance of the algorithm
B. It has no effect on the performance of the algorithm
C. It worsens the performance of the algorithm
D. It depends on the type of similarity measure used
view answer:
A. It improves the performance of the algorithm
Explanation:
Feature scaling generally improves the performance of instance-based learning algorithms by ensuring that all features contribute equally to the similarity measure.
13.
In instance-based learning, how can missing values be handled?
A. By removing instances with missing values
B. By imputing missing values using the mean or median
C. By using similarity measures that can handle missing values
D. All of the above
view answer:
D. All of the above
Explanation:
In instance-based learning, missing values can be handled by removing instances with missing values, imputing missing values using the mean or median, or using similarity measures that can handle missing values.
14.
Which of the following is an advantage of instance-based learning over model-based learning?
A. Faster training time
B. Faster prediction time
C. Better handling of noisy data
D. Lower memory requirements
view answer:
A. Faster training time
Explanation:
Instance-based learning has a faster training time compared to model-based learning, as it does not involve creating a model from the training data.
15.
In which of the following scenarios is instance-based learning likely to perform better than model-based learning?
A. When the data is linearly separable
B. When the data has complex non-linear patterns
C. When the data has a large number of features
D. When the data has a large number of instances
view answer:
B. When the data has complex non-linear patterns
Explanation:
Instance-based learning is likely to perform better than model-based learning when the data has complex non-linear patterns, as it directly uses the training instances for classification without making any assumptions about the underlying data distribution.
16.
Which of the following instance-based learning algorithms uses a network of instances to represent the training data?
A. k-Nearest Neighbors
B. Learning Vector Quantization (LVQ)
C. Self-Organizing Maps (SOM)
D. Radial Basis Function Networks (RBFN)
view answer:
B. Learning Vector Quantization (LVQ)
Explanation:
Learning Vector Quantization (LVQ) uses a network of instances (codebook vectors) to represent the training data, with instances connected to their nearest neighbors.
17.
Which of the following methods can be used to select the optimal 'k' value in k-Nearest Neighbors algorithm?
A. Cross-validation
B. Grid search
C. Random search
D. All of the above
view answer:
D. All of the above
Explanation:
Cross-validation, grid search, and random search can all be used to select the optimal 'k' value in k-Nearest Neighbors algorithm.
18.
What is the primary difference between instance-based learning and model-based learning?
A. Instance-based learning uses training data to create a model, while model-based learning relies on instances for classification.
B. Instance-based learning does not create a model, while model-based learning creates a model from the training data.
C. Instance-based learning only works on numeric data, while model-based learning works on both numeric and categorical data.
D. Instance-based learning is a type of supervised learning, while model-based learning is a type of unsupervised learning.
view answer:
B. Instance-based learning does not create a model, while model-based learning creates a model from the training data.
Explanation:
Instance-based learning directly uses the training instances for classification, while model-based learning creates a model from the training data.
19.
Which algorithm is an example of instance-based learning?
A. Decision Trees
B. k-Nearest Neighbors (k-NN)
C. Support Vector Machines
D. Linear Regression
view answer:
B. k-Nearest Neighbors (k-NN)
Explanation:
k-Nearest Neighbors (k-NN) is an example of instance-based learning, as it directly uses the training instances to classify new data points.
20.
What type of distance metric is commonly used in k-Nearest Neighbors algorithm?
A. Manhattan distance
B. Euclidean distance
C. Minkowski distance
D. Both A and B
view answer:
D. Both A and B
Explanation:
Both Manhattan distance and Euclidean distance are commonly used distance metrics in k-Nearest Neighbors algorithm.
21.
In k-Nearest Neighbors (k-NN), what does 'k' represent?
A. The number of instances in the dataset
B. The number of features in the dataset
C. The number of nearest instances considered for classification
D. The number of possible labels for classification
view answer:
C. The number of nearest instances considered for classification
Explanation:
'k' represents the number of nearest instances considered for classification in k-Nearest Neighbors algorithm.
22.
Which of the following is true about instance-based learning algorithms?
A. They are computationally expensive during training
B. They are computationally expensive during prediction
C. They require less memory than model-based algorithms
D. They are not affected by the curse of dimensionality
view answer:
B. They are computationally expensive during prediction
Explanation:
Instance-based learning algorithms are computationally expensive during prediction, as they need to compare new data points with all instances in the training data.
23.
What is the primary disadvantage of using a large 'k' value in k-Nearest Neighbors algorithm?
A. Overfitting
B. Underfitting
C. Increased computation time
D. Decreased accuracy
view answer:
B. Underfitting
Explanation:
Using a large 'k' value in k-Nearest Neighbors algorithm may lead to underfitting, as the model becomes too general and less sensitive to local patterns in the data.
24.
Which of the following is NOT a step in the k-Nearest Neighbors algorithm?
A. Calculate the distance between the new data point and all training instances
B. Select the k nearest instances
C. Train a model using the k nearest instances
D. Determine the majority class among the k nearest instances
view answer:
C. Train a model using the k nearest instances
Explanation:
k-Nearest Neighbors algorithm does not involve training a model; it directly uses the k nearest instances for classification.
25.
Which of the following techniques can be used to address the curse of dimensionality in instance-based learning algorithms?
A. Feature selection
B. Feature extraction
C. Dimensionality reduction
D. All of the above
view answer:
D. All of the above
Explanation:
Feature selection, feature extraction, and dimensionality reduction are all techniques that can be used to address the curse of dimensionality in instance-based learning algorithms.
26.
In the context of k-Nearest Neighbors, how is a regression problem handled?
A. By selecting the majority class among the k nearest instances
B. By calculating the mean of the target values of the k nearest instances
C. By calculating the mode of the target values of the k nearest instances
D. By calculating the median of the target values of the k nearest instances
view answer:
B. By calculating the mean of the target values of the k nearest instances
Explanation:
In the context of k-Nearest Neighbors, a regression problem is handled by calculating the mean of the target values of the k nearest instances.
27.
Which of the following is a disadvantage of using a small 'k' value in k-Nearest Neighbors algorithm?
A. Overfitting
B. Underfitting
C. Increased computation time
D. Decreased accuracy
view answer:
A. Overfitting
Explanation:
Using a small 'k' value in k-Nearest Neighbors algorithm may lead to overfitting, as the model becomes too sensitive to noise in the training data.
28.
How can the k-Nearest Neighbors algorithm be adapted for multi-label classification problems?
A. By selecting the majority class for each label among the k nearest instances
B. By selecting the majority class among the k nearest instances for each label
C. By selecting the class with the highest probability for each label among the k nearest instances
D. By selecting the class with the highest probability among the k nearest instances for each label
view answer:
A. By selecting the majority class for each label among the k nearest instances
Explanation:
The k-Nearest Neighbors algorithm can be adapted for multi-label classification problems by selecting the majority class for each label among the k nearest instances.
29.
In instance-based learning, what is the purpose of using a weighted voting scheme?
A. To give more importance to closer instances when determining the class of a new data point
B. To give more importance to further instances when determining the class of a new data point
C. To give equal importance to all instances when determining the class of a new data point
D. To give more importance to instances with more features when determining the class of a new data point
view answer:
A. To give more importance to closer instances when determining the class of a new data point
Explanation:
In instance-based learning, a weighted voting scheme is used to give more importance to closer instances when determining the class of a new data point.
30.
Which of the following is NOT a similarity measure used in instance-based learning algorithms?
A. Euclidean distance
B. Cosine similarity
C. Pearson correlation coefficient
D. Linear regression
view answer:
D. Linear regression
Explanation:
Linear regression is not a similarity measure used in instance-based learning algorithms; it is a model-based learning algorithm.
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