K-Nearest Neighbour Classifier. Izabela Moise, Evangelos Pournaras, Dirk Helbing
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1 K-Nearest Neighbour Classifier Izabela Moise, Evangelos Pournaras, Dirk Helbing Izabela Moise, Evangelos Pournaras, Dirk Helbing 1
2 Reminder Supervised data mining Classification Decision Trees Izabela Moise, Evangelos Pournaras, Dirk Helbing 2
3 K-Nearest Neighbour (knn) Classifier Izabela Moise, Evangelos Pournaras, Dirk Helbing 3
4 Classification steps 1. Training phase: a model is constructed from the training instances. classification algorithm finds relationships between predictors and targets relationships are summarised in a model 2. Testing phase: test the model on a test sample whose class labels are known but not used for training the model 3. Usage phase: use the model for classification on new data whose class labels are unknown Izabela Moise, Evangelos Pournaras, Dirk Helbing 4
5 Instance-based Classification Main idea: Similar instances have similar classification no clear separation between the three phases of classification also called lazy classification, as opposed to eager classification Izabela Moise, Evangelos Pournaras, Dirk Helbing 5
6 Eager vs Lazy Classification Eager Model is computed before classification Model is independent of the test instance Test instance is not included in the training data Avoids too much work at classification time Model is not accurate for each instance Lazy Model is computed during classification Model is dependent on the test instance Test instance is included in the training data High accuracy for models at each instance level Izabela Moise, Evangelos Pournaras, Dirk Helbing 6
7 k-nearest Neighbor (knn) Learning by analogy: Tell me who your friends are and I ll tell you who you are an instance is assigned to the most common class among the instances similar to it 1. how to measure similarity between instances 2. how to choose the most common class Izabela Moise, Evangelos Pournaras, Dirk Helbing 7
8 How does it work? Izabela Moise, Evangelos Pournaras, Dirk Helbing 8
9 How does it work? Izabela Moise, Evangelos Pournaras, Dirk Helbing 8
10 Comparing Objects Problem : measure similarity between instances different types of data: numbers colours, geolocation, booleans etc. Solution : convert all features of the instances into numerical values represent instances as vectors of features in an n-dimensional space Izabela Moise, Evangelos Pournaras, Dirk Helbing 9
11 Comparing Objects Problem : measure similarity between instances vs. text similarity different types of data: numbers colours, geolocation, booleans etc. Solution : convert all features of the instances into numerical values represent instances as vectors of features in an n-dimensional space Izabela Moise, Evangelos Pournaras, Dirk Helbing 9
12 Comparing Objects Problem : measure similarity between instances vs. text similarity different types of data: numbers colours, geolocation, booleans etc. Solution : convert all features of the instances into numerical values represent instances as vectors of features in an n-dimensional space Izabela Moise, Evangelos Pournaras, Dirk Helbing 9
13 Comparing Objects Problem : measure similarity between instances vs. text similarity different types of data: numbers colours, geolocation, booleans etc. Solution : convert all features of the instances into numerical values represent instances as vectors of features in an n-dimensional space Izabela Moise, Evangelos Pournaras, Dirk Helbing 9
14 Comparing Objects Problem : measure similarity between instances vs. text similarity different types of data: numbers colours, geolocation, booleans etc. Solution : convert all features of the instances into numerical values represent instances as vectors of features in an n-dimensional space Izabela Moise, Evangelos Pournaras, Dirk Helbing 9
15 An Example: Izabela Moise, Evangelos Pournaras, Dirk Helbing 10
16 Distance Metrics 1. Euclidean Distance 2. Manhattan Distance 3. Minkowski Distance Izabela Moise, Evangelos Pournaras, Dirk Helbing 11
17 Choosing k Classification is sensitive to the correct selection of k if k is too small overfitting small k? larger k? algorithm performs too good on the training set, compared to its true performance on unseen test data Izabela Moise, Evangelos Pournaras, Dirk Helbing 12
18 Choosing k Classification is sensitive to the correct selection of k if k is too small overfitting algorithm performs too good on the training set, compared to its true performance on unseen test data small k? less stable, influenced by noise larger k? less precise, higher bias Izabela Moise, Evangelos Pournaras, Dirk Helbing 12
19 Choosing k Classification is sensitive to the correct selection of k if k is too small overfitting algorithm performs too good on the training set, compared to its true performance on unseen test data small k? less stable, influenced by noise larger k? less precise, higher bias k= 2 n Izabela Moise, Evangelos Pournaras, Dirk Helbing 12
20 Pros and Cons Pros: simple to implement and use robust to noisy data by averaging k-nearest neighbours knn classification is based solely on local information the decision boundaries can be of arbitrary shapes Izabela Moise, Evangelos Pournaras, Dirk Helbing 13
21 Pros and Cons Cons: X curse of dimensionality: distance can be dominated by irrelevant attributes X O(n) for each instance to be classified X more expensive to classify a new instance than with a model Izabela Moise, Evangelos Pournaras, Dirk Helbing 14
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k Nearest Neighbors Super simple idea! Instance-based learning as opposed to model-based (no pre-processing)
k Nearest Neighbors k Nearest Neighbors To classify an observation: Look at the labels of some number, say k, of neighboring observations. The observation is then classified based on its nearest neighbors