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Nearest neighbors

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zekeLabs Technologies
zekeLabs Technologies

Nearest neighbors

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zekeLabs Nearest Neighbors
“Goal - Become a Data Scientist” “A Dream becomes a Goal when action is taken towards its achievement” - Bo Bennett “The Plan” “A Goal without a Plan is just a wish”
● Fundamentals of Nearest Neighbour ● Nearest Neighbours for Unsupervised Learning ● Nearest Neighbours for Classification ● Nearest Neighbours for Regression ● Nearest Centroid Classifier Agenda
Fundamentals of Nearest Neighbour ● The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these. ● The number of samples can be a user-defined constant (k-nearest neighbor learning), or vary based on the local density of points (radius-based neighbor learning). ● Being a non-parametric method, it is often successful in classification situations where the decision boundary is very irregular. ● Neighbors-based methods are known as non-generalizing machine learning methods, since they simply “remember” all of its training data
Algorithms of Nearest Neighbours ● Algorithms to remember data ● Brute Force ● K-D Tree ● Ball Tree
Nearest Neighbours Unsupervised Learning ● During fit, it just stores the training data. ● For a query point just finds out nearest k neighbours
Nearest Neighbours Classification ● Instance based learning & does not construct a generalized model. ● A query point is assigned the data class which has the most representatives within the nearest neighbors of the point. ● Two major types ● KNeighboursClassifier ( based on configured k ) ● RadiusNeighbourClassifier ( based on configured r ) ● Weights can be ‘uniform’ or ‘distance’. It assigns weights proportional to the inverse of the distance from the query point.
Classification k - factor
Classification weight factor
Nearest Neighbours Regression ● Data labels are continues ● The label assigned to a query point is computed based the mean of the labels of its nearest neighbors. ● Two types of regressors ● KNeighbourRegressor ● RadiusNeighbourRegressor ● Weights concept holds good here similar to classification
Regression weight- Factor
Application ● Multi-output regression using nearest neighbours regressors
Nearest Centroid Classifier ● The NearestCentroid classifier is a simple algorithm that represents each class by the centroid of its members
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Nearest neighbors

  • 2. “Goal - Become a Data Scientist” “A Dream becomes a Goal when action is taken towards its achievement” - Bo Bennett “The Plan” “A Goal without a Plan is just a wish”
  • 3. ● Fundamentals of Nearest Neighbour ● Nearest Neighbours for Unsupervised Learning ● Nearest Neighbours for Classification ● Nearest Neighbours for Regression ● Nearest Centroid Classifier Agenda
  • 4. Fundamentals of Nearest Neighbour ● The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these. ● The number of samples can be a user-defined constant (k-nearest neighbor learning), or vary based on the local density of points (radius-based neighbor learning). ● Being a non-parametric method, it is often successful in classification situations where the decision boundary is very irregular. ● Neighbors-based methods are known as non-generalizing machine learning methods, since they simply “remember” all of its training data
  • 5. Algorithms of Nearest Neighbours ● Algorithms to remember data ● Brute Force ● K-D Tree ● Ball Tree
  • 6. Nearest Neighbours Unsupervised Learning ● During fit, it just stores the training data. ● For a query point just finds out nearest k neighbours
  • 7. Nearest Neighbours Classification ● Instance based learning & does not construct a generalized model. ● A query point is assigned the data class which has the most representatives within the nearest neighbors of the point. ● Two major types ● KNeighboursClassifier ( based on configured k ) ● RadiusNeighbourClassifier ( based on configured r ) ● Weights can be ‘uniform’ or ‘distance’. It assigns weights proportional to the inverse of the distance from the query point.
  • 10. Nearest Neighbours Regression ● Data labels are continues ● The label assigned to a query point is computed based the mean of the labels of its nearest neighbors. ● Two types of regressors ● KNeighbourRegressor ● RadiusNeighbourRegressor ● Weights concept holds good here similar to classification
  • 12. Application ● Multi-output regression using nearest neighbours regressors
  • 13. Nearest Centroid Classifier ● The NearestCentroid classifier is a simple algorithm that represents each class by the centroid of its members