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Questions and Answers
For K=1, the nearest customer's ID is ______ with Personal Loan =1;
9
The extreme case of k = ______ is the same as the “naïve rule”.
n
Too small K captures not only local ______ but also noise.
structure
Euclidean ______ is used to measure the distance between data points.
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The value of K is crucial to find a good balance between ______ and under-fitting.
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Cross-validation is another more effective way to determine a good ______ value.
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To get the optimal value of K, divide the initial dataset into ______ and validation datasets.
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For K=5, 3 of 5 of the nearest customers have their Personal Loan = ______.
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The table above is an example of data used for a ______ algorithm, which is a type of supervised learning algorithm.
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The performance of a KNN model can be measured using metrics such as ______ and accuracy.
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One way to prevent overfitting in a KNN model is to use ______ to reduce the dimensionality of the data.
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In KNN, the distance between data points is typically measured using ______ such as Euclidean distance or Manhattan distance.
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The value of K in a KNN model is a hyperparameter that needs to be ______ in order to achieve optimal performance.
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The KNN algorithm is often used for ______ classification, as shown in the example above.
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To prevent overfitting, we need to ____________________ the data into training and validation sets.
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The error rate of the model is calculated by comparing the predicted classification with the ____________________ value.
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The KNN algorithm is used for ____________________ classification.
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The Euclidean Distance is used to measure the distance between the records in the ____________________ set and the validation set.
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The value of K is selected based on the lowest ____________________ rate in the validation dataset.
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The goal is to choose the value of K that has the ____________________ error rate in the validation dataset.
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If the predicted classification is 1 and the true classification is 0, it is considered a ____________________ classification.
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The error rate is calculated as the number of ____________________ classifications divided by the total number of records.
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The process of validating the model involves calculating the error rate for different values of ____________________.
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The validation dataset is used to evaluate the performance of the model and to prevent ____________________.
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Study Notes
Classification using KNN
- KNN (K-Nearest Neighbors) is a classification algorithm that classifies a new customer based on the majority vote of its neighbors.
- The algorithm works by calculating the Euclidean distance between the new customer and existing customers in the dataset.
- The Euclidean distance is a measure of the straight-line distance between two points in n-dimensional space.
Choosing the Number of Neighbors: K
- The value of K is crucial in finding a good balance between overfitting and underfitting.
- Too small a value of K (e.g. 1, 3) captures not only local structure in data but also noise.
- Too large a value of K (e.g. 10) destroys the locality of the estimation since farther examples are taken into account, increasing the computational burden.
- Historically, the optimal K for most datasets has been between 3-10.
- Cross-validation is a more effective way to determine a good K value by using an independent dataset to validate the K value.
Cross-Validation
- Divide the initial dataset into training and validation datasets.
- Classify the cases in the validation dataset using different values of K.
- Choose the value of K which has the lowest error rate in the validation dataset.
Example of Classification using KNN
- Given a new customer with attributes (Age, Experience, Income, Family, CCAvg), calculate the Euclidean distance between the new customer and existing customers in the dataset.
- For K=1, the nearest customer is classified as 1, so the new customer is classified as 1.
- For K=5, the 5 nearest customers are classified as 1, 0, 1, 0, 0, so the new customer is classified as 0.
Example of Cross-Validation
- Divide the dataset into training and validation sets.
- Calculate the Euclidean distance between every record in the validation set and every record in the training set.
- Calculate the classification error for different K values (e.g. K=3, K=5).
- Choose the K value with the lowest error rate (e.g. 75%).
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Description
This quiz is based on an example of classification using the K-Nearest Neighbors (KNN) algorithm, which is a popular machine learning technique. It presents a scenario with given distances and asks for the correct classification.
