Setting Parameters: Affinity
Well, that was not the result we were looking for. Can we improve it? Can we make the clustering algorithm learn to differ such structures?
The answer is yes - we need to set some parameters within the SpectralClustering
function. The parameter we should change is affinity
. This parameter defines how should affinity matrix be built (the math explanation of this is outside the scope of this course). By default, the parameter's value is 'rbf'
. If we want to differ the clusters with such a structure as in the previous chapter, we should consider the 'nearest_neighbors'
value of the parameter.
Swipe to start coding
- Import
SpectralClustering
function fromsklearn.cluster
. - Create a
SpectralClustering
model object with 4 clusters and set theaffinity
parameter to'nearest_neighbors'
. - Fit the
data
to themodel
and predict the labels. Save predicted labels as the'prediction'
column ofdata
. - Build the
seaborn
scatter plot with'x'
column ofdata
on the x-axis,'y'
column ofdata
on the y-axis for each value of'prediction'
. Then, display the plot.
Solution
Merci pour vos commentaires !
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Setting Parameters: Affinity
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Well, that was not the result we were looking for. Can we improve it? Can we make the clustering algorithm learn to differ such structures?
The answer is yes - we need to set some parameters within the SpectralClustering
function. The parameter we should change is affinity
. This parameter defines how should affinity matrix be built (the math explanation of this is outside the scope of this course). By default, the parameter's value is 'rbf'
. If we want to differ the clusters with such a structure as in the previous chapter, we should consider the 'nearest_neighbors'
value of the parameter.
Swipe to start coding
- Import
SpectralClustering
function fromsklearn.cluster
. - Create a
SpectralClustering
model object with 4 clusters and set theaffinity
parameter to'nearest_neighbors'
. - Fit the
data
to themodel
and predict the labels. Save predicted labels as the'prediction'
column ofdata
. - Build the
seaborn
scatter plot with'x'
column ofdata
on the x-axis,'y'
column ofdata
on the y-axis for each value of'prediction'
. Then, display the plot.
Solution
Merci pour vos commentaires !
Awesome!
Completion rate improved to 3.57single