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Cluster Analysis in Python

bookSetting 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.

Tarea

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  1. Import SpectralClustering function from sklearn.cluster.
  2. Create a SpectralClustering model object with 4 clusters and set the affinity parameter to 'nearest_neighbors'.
  3. Fit the data to the model and predict the labels. Save predicted labels as the 'prediction' column of data.
  4. Build the seaborn scatter plot with 'x' column of data on the x-axis, 'y' column of data on the y-axis for each value of 'prediction'. Then, display the plot.

Solución

¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

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Sección 4. Capítulo 3
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bookSetting 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.

Tarea

Swipe to start coding

  1. Import SpectralClustering function from sklearn.cluster.
  2. Create a SpectralClustering model object with 4 clusters and set the affinity parameter to 'nearest_neighbors'.
  3. Fit the data to the model and predict the labels. Save predicted labels as the 'prediction' column of data.
  4. Build the seaborn scatter plot with 'x' column of data on the x-axis, 'y' column of data on the y-axis for each value of 'prediction'. Then, display the plot.

Solución

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¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

close

Awesome!

Completion rate improved to 3.57
Sección 4. Capítulo 3
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