Part 1 - K Nearest Neighbor KNN Solved Example Cosine Similarity Manhattan Distance Mahesh Huddar
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6 ماه پیش
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Part 1 - Similarity-Based Learning
Part 1 - Similarity-Based Learning – K Nearest Neighbor - KNN EuclideanDistance, Weighted EuclideanDistance, Cosine Similarity, Manhattan Distance Solved Example by Mahesh Huddar
Part 2: Part 2 - K Nearest Neighbor KNN Sol...
Email spam filtering models often use a bag-of-words representation for emails. In a bag-of-words representation, the descriptive features that describe a document (in our case, an email) each represent how many times a particular word occurs in the document. One descriptive feature is included for each word in a predefined dictionary. The dictionary is typically defined as the complete set of words that occur in the training dataset. The table below lists the bag-of-words representation for the following five emails and a target feature, SPAM, whether they are spam emails or genuine emails:
1. money, money, money
2. free money for free gambling fun
3. gambling for fun
4. machine learning for fun, fun, fun
5. free machine learning
What target level would the nearest neighbor model using Euclidean distance return for the following email: “machine learning for free”? With k =1 and k = 3.
What target level would a weighted k-NN model with k = 3 and using a weighting scheme of the reciprocal of the squared Euclidean distance between the neighbor and the query, return for the query?
What target level would a k-NN model with k = 3 and using Manhattan distance return for the same query?
What target level would a 3-NN model using cosine similarity return for the query?
The following concepts are discussed:
______________________________
Similarity-Based Learning,
K Nearest Neighbor,
KNN,
EuclideanDistance,
Weighted EuclideanDistance,
Cosine Similarity,
Manhattan Distance,
Similarity-Based Learning Solved Example,
K Nearest Neighbor Solved Example,
KNN Solved Example,
Euclidean Distance Solved Example,
Weighted Euclidean Distance Solved Example,
Cosine Similarity Solved Example,
Manhattan Distance Solved Example,
Similarity-Based Learning spam classification,
K Nearest Neighbor spam classification,
KNN spam classification
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Part 2: Part 2 - K Nearest Neighbor KNN Sol...
Email spam filtering models often use a bag-of-words representation for emails. In a bag-of-words representation, the descriptive features that describe a document (in our case, an email) each represent how many times a particular word occurs in the document. One descriptive feature is included for each word in a predefined dictionary. The dictionary is typically defined as the complete set of words that occur in the training dataset. The table below lists the bag-of-words representation for the following five emails and a target feature, SPAM, whether they are spam emails or genuine emails:
1. money, money, money
2. free money for free gambling fun
3. gambling for fun
4. machine learning for fun, fun, fun
5. free machine learning
What target level would the nearest neighbor model using Euclidean distance return for the following email: “machine learning for free”? With k =1 and k = 3.
What target level would a weighted k-NN model with k = 3 and using a weighting scheme of the reciprocal of the squared Euclidean distance between the neighbor and the query, return for the query?
What target level would a k-NN model with k = 3 and using Manhattan distance return for the same query?
What target level would a 3-NN model using cosine similarity return for the query?
The following concepts are discussed:
______________________________
Similarity-Based Learning,
K Nearest Neighbor,
KNN,
EuclideanDistance,
Weighted EuclideanDistance,
Cosine Similarity,
Manhattan Distance,
Similarity-Based Learning Solved Example,
K Nearest Neighbor Solved Example,
KNN Solved Example,
Euclidean Distance Solved Example,
Weighted Euclidean Distance Solved Example,
Cosine Similarity Solved Example,
Manhattan Distance Solved Example,
Similarity-Based Learning spam classification,
K Nearest Neighbor spam classification,
KNN spam classification
********************************
1. Blog / Website: https://www.vtupulse.com/
2. Like Facebook Page: Facebook: VTUPulse
3. Follow us on Instagram: Instagram: vtupulse
4. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates
6 ماه پیش
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