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Classifying with scikit-learn Estimators
In the figure (a), on the left-hand side, we would usually expect the test sample
(the triangle) to be classified as a circle. However, if n_neighbors is 1, the single
red diamond in this area (likely a noisy sample) causes the sample to be predicted
as being a diamond, while it appears to be in a red area. In the figure (b), on the
right-hand side, we would usually expect the test sample to be classified as a
diamond. However, if n_neighbors is 7, the three nearest neighbors (which
are all diamonds) are overridden by the large number of circle samples.
If we want to test a number of values for the n_neighbors parameter, for example,
each of the values from 1 to 20, we can rerun the experiment many times by setting
n_neighbors and observing the result:
avg_scores = []
all_scores = []
parameter_values = list(range(1, 21)) # Include 20
for n_neighbors in parameter_values:
estimator = KNeighborsClassifier(n_neighbors=n_neighbors)
scores = cross_val_score(estimator, X, y, scoring='accuracy')
Compute and store the average in our list of scores. We also store the full set of
scores for later analysis:
avg_scores.append(np.mean(scores))
all_scores.append(scores)
We can then plot the relationship between the value of n_neighbors and the
accuracy. First, we tell the IPython Notebook that we want to show plots inline
in the notebook itself:
%matplotlib inline
We then import pyplot from the matplotlib library and plot the parameter values
alongside average scores:
from matplotlib import pyplot as plt plt.plot(parameter_values,
avg_scores, '-o')
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