Hello,
I have posted this question in Stackoverflow and did not get an answer.
This seems to be a basic usage question and am therefore sending it here.
I have following code snippet that attempts to do a grid search in which
one of the grid parameters are the distance metrics to be used for the
KNN algorithm. The example below fails if I use "wminkowski",
"seuclidean" or "mahalanobis" distances metrics.
# Define the parameter values that should be searched
k_range = range(1,31)
weights = ['uniform' , 'distance']
algos = ['auto', 'ball_tree', 'kd_tree', 'brute']
leaf_sizes = range(10, 60, 10)
metrics = ["euclidean", "manhattan", "chebyshev", "minkowski",
"mahalanobis"]
param_grid = dict(n_neighbors = list(k_range), weights = weights,
algorithm = algos, leaf_size = list(leaf_sizes), metric=metrics)
param_grid
# Instantiate the algorithm
knn = KNeighborsClassifier(n_neighbors=10)
# Instantiate the grid
grid = GridSearchCV(knn, param_grid=param_grid, cv=10,
scoring='accuracy', n_jobs=-1)
# Fit the models using the grid parameters
grid.fit(X,y)
I assume this is because I have to set or define the ranges for the
various distance parameters (for example p, w for “wminkowski” -
WMinkowskiDistance ). The "minkowski" distance may be working because
its "p" parameter has the default 2.
So my questions are:
1. Can we set the range of parameters for the distance metrics for the
grid search and if so how?
2. Can we set the value of a parameters for the distance metrics for the
grid search and if so how?
Hope the question is clear.
TIA
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