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The objective of this lab is to program and test two classication algorithms, very simple but very eective: the K - Nearest Neighbor ( KNN
The objective of this lab is to program and test two classication algorithms, very simple but very eective: the KNearest Neighbor KNN algorithm and the Classier Bayesian Naive CBN We are studying here only the simplest versions of these algorithms. For this lab we will need to import sklearn and numpy. The tests can be done on sklearn's predened data that comes with their class labels target for example: iris datasets.loadiris X iris.data Y iris.target A Nearest neighbor The Nearest Neighbor algorithm is a very simple classication algorithm which is based on the following principle: the class of each test data to be classied must be the class of the closest most similar data among the training data. List of useful functions: metrics.pairwise.euclideandistances: calculates distances between data. argsort: returns the indices of the ordered vector argmin, argmax: returns the indices of the minimummaximum values neighbors.KNeighborsClassifier: K Nearest Neighbors alg. of sklearn Test the function of the K Closest Neighbors of sklearn with here K Are the results dierent? Test with other values of K
The objective of this lab is to program and test two classication algorithms,
very simple but very eective: the KNearest Neighbor KNN algorithm and the
Classier Bayesian Naive CBN We are studying here only the simplest versions
of these algorithms. For this lab we will need to import sklearn and numpy. The
tests can be done on sklearn's predened data that comes with their class labels
target for example:
iris datasets.loadiris
X iris.data
Y iris.target
A Nearest neighbor
The Nearest Neighbor algorithm is a very simple classication algorithm which is
based on the following principle: the class of each test data to be classied must
be the class of the closest most similar data among the training data. List of
useful functions:
metrics.pairwise.euclideandistances: calculates distances between data.
argsort: returns the indices of the ordered vector
argmin, argmax: returns the indices of the minimummaximum values
neighbors.KNeighborsClassifier: K Nearest Neighbors alg. of sklearn
Test the function of the K Closest Neighbors of sklearn with here K
Are the results dierent? Test with other values of K
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