Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Lære Setting Parameters: Affinity | Spectral Clustering
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.

Oppgave

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.

Løsning

Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 4. Kapittel 3
single

single

Spør AI

expand

Spør AI

ChatGPT

Spør om hva du vil, eller prøv ett av de foreslåtte spørsmålene for å starte chatten vår

close

Awesome!

Completion rate improved to 3.57

bookSetting Parameters: Affinity

Sveip for å vise menyen

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.

Oppgave

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.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

close

Awesome!

Completion rate improved to 3.57
Seksjon 4. Kapittel 3
single

single

some-alt