Podcast
Questions and Answers
In classification, what is the purpose of constructing a model?
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?
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?
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?
What is the purpose of estimating classifier accuracy and trying to avoid overfitting?
How does instance-based learning differ from learning methods that construct an explicit description of the target function?
How does instance-based learning differ from learning methods that construct an explicit description of the target function?
What is a key characteristic of instance-based learning methods?
What is a key characteristic of instance-based learning methods?
How do instance-based methods estimate the target function?
How do instance-based methods estimate the target function?
What does learning consist of in instance-based algorithms?
What does learning consist of in instance-based algorithms?
What is a significant advantage of instance-based learning?
What is a significant advantage of instance-based learning?
One of the disadvantages of instance-based learning is:
One of the disadvantages of instance-based learning is:
What issue arises in instance-based learning when the target concept depends only on a few of the many available attributes?
What issue arises in instance-based learning when the target concept depends only on a few of the many available attributes?
Which of the following best describes the k-Nearest Neighbor (KNN) algorithm?
Which of the following best describes the k-Nearest Neighbor (KNN) algorithm?
What is the primary calculation involved in the k-NN classifier algorithm to make predictions?
What is the primary calculation involved in the k-NN classifier algorithm to make predictions?
What is the 'Curse of Dimensionality' in the context of Nearest Neighbor algorithms?
What is the 'Curse of Dimensionality' in the context of Nearest Neighbor algorithms?
In k-NN, how does the presence of irrelevant features affect the algorithm's performance?
In k-NN, how does the presence of irrelevant features affect the algorithm's performance?
What does the k-NN algorithm identify given N training vectors?
What does the k-NN algorithm identify given N training vectors?
In the context of the k-NN algorithm, what is 'parameter tuning'?
In the context of the k-NN algorithm, what is 'parameter tuning'?
When k = 1 in k-NN, what does each training vector define in space?
When k = 1 in k-NN, what does each training vector define in space?
What is the first step in the k-NN classifier algorithm?
What is the first step in the k-NN classifier algorithm?
For a 2-class problem, what is a common recommendation for choosing the value of 'k' in k-NN?
For a 2-class problem, what is a common recommendation for choosing the value of 'k' in k-NN?
Why should 'k' not be a multiple of the number of classes?
Why should 'k' not be a multiple of the number of classes?
What is a main drawback of k-NN regarding its computational complexity?
What is a main drawback of k-NN regarding its computational complexity?
Choosing a small value for K in k-NN typically leads to:
Choosing a small value for K in k-NN typically leads to:
Choosing a large value for K in k-NN typically leads to:
Choosing a large value for K in k-NN typically leads to:
How is the value of k determined in k-NN?
How is the value of k determined in k-NN?
When is generally a good scenerio to use KNN:
When is generally a good scenerio to use KNN:
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?
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?
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?
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?
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?
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?
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.
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.
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?
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?
What is the first step while using KNN to determining if a test patient is diabetic, given examples of BMI, Age, and Sugar?
What is the first step while using KNN to determining if a test patient is diabetic, given examples of BMI, Age, and Sugar?
If there are 10 examples in a dataset with 2 classes, what value of k would you pick?
If there are 10 examples in a dataset with 2 classes, what value of k would you pick?
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?
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?
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?
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?
What distance parameters are needed to estimate a class using solved KNN.
What distance parameters are needed to estimate a class using solved KNN.
What are the steps required to estimate solved KNN except which ones?
What are the steps required to estimate solved KNN except which ones?
Flashcards
What is Classification?
What is Classification?
A data analysis task where a model is constructed to predict class labels (categories).
The Basics General Approach
The Basics General Approach
A two-step process: Learning (training) and Classification (testing).
Learning (training) step
Learning (training) step
Construct a classification model from a training dataset.
Classification step
Classification step
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Instance-based Learning
Instance-based Learning
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Learning in Instance-based Algorithms
Learning in Instance-based Algorithms
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Advantages of Instance-based learning
Advantages of Instance-based learning
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"lazy" learning methods
"lazy" learning methods
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Disadvantages of Instance-based learning
Disadvantages of Instance-based learning
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K-NN Classifier
K-NN Classifier
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K-Nearest Neighbor Classifier
K-Nearest Neighbor Classifier
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Curse of Dimensionality
Curse of Dimensionality
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K-Nearest Neighbor Classifier
K-Nearest Neighbor Classifier
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Choosing K
Choosing K
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Voronoi partition of the space
Voronoi partition of the space
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How to choose factor K
How to choose factor K
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Euclidean distance
Euclidean distance
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KNN
KNN
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Hamming distance
Hamming distance
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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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