Valutare il clustering dei cereali

Nell'esercizio precedente, dal grafico dell'inerzia hai visto che 3 è un buon numero di cluster per i dati sui cereali. In effetti, i campioni provengono da un mix di 3 varietà diverse: "Kama", "Rosa" e "Canadian". In questo esercizio, raggruppa i campioni in tre cluster e confronta i cluster con le varietà usando una tabella a doppia entrata.

Hai a disposizione l'array samples con i campioni e una lista varieties con la varietà corrispondente a ciascun campione. Pandas (pd) e KMeans sono già stati importati per te.

Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

Visualizza il corso

Istruzioni dell'esercizio

Crea un modello KMeans chiamato model con 3 cluster.
Usa il metodo .fit_predict() di model per addestrarlo su samples e ricavare le etichette dei cluster. Usare .fit_predict() equivale a usare .fit() seguito da .predict().
Crea un DataFrame df con due colonne chiamate 'labels' e 'varieties', usando rispettivamente labels e varieties come valori. Questo è già stato fatto per te.
Usa la funzione pd.crosstab() su df['labels'] e df['varieties'] per contare quante volte ciascuna varietà coincide con ciascuna etichetta di cluster. Assegna il risultato a ct.
Premi Invia per vedere la tabella a doppia entrata!

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a KMeans model with 3 clusters: model
model = ____

# Use fit_predict to fit model and obtain cluster labels: labels
labels = ____

# Create a DataFrame with labels and varieties as columns: df
df = pd.DataFrame({'labels': labels, 'varieties': varieties})

# Create crosstab: ct
ct = ____

# Display ct
print(ct)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Apprendimento non supervisionato in Python

IntermediárioNível de habilidade

4.8+

Inizia il corso gratis

Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Exercise 1: Unsupervised Learning Exercise 2: Quanti cluster?Exercise 3: Clustering di punti 2D Exercise 4: Esamina il tuo clustering Exercise 5: Valutare un clustering Exercise 6: Quanti cluster di cereali?Exercise 7: Valutare il clustering dei cereali

Esercizio in corso

Exercise 8: Trasformare le caratteristiche per cluster più efficaci Exercise 9: Scalare i dati sui pesci per il clustering Exercise 10: Clustering dei dati sui pesci Exercise 11: Clustering dei titoli azionari con KMeans Exercise 12: Quali titoli si muovono insieme?

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Exercise 1: Visualizing hierarchies Exercise 2: How many merges?Exercise 3: Hierarchical clustering of the grain data Exercise 4: Hierarchies of stocks Exercise 5: Cluster labels in hierarchical clustering Exercise 6: Which clusters are closest?Exercise 7: Different linkage, different hierarchical clustering!Exercise 8: Intermediate clusterings Exercise 9: Extracting the cluster labels Exercise 10: t-SNE for 2-dimensional maps Exercise 11: t-SNE visualization of grain dataset Exercise 12: A t-SNE map of the stock market

Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Exercise 1: Visualizing the PCA transformation Exercise 2: Correlated data in nature Exercise 3: Decorrelating the grain measurements with PCA Exercise 4: Principal components Exercise 5: Intrinsic dimension Exercise 6: The first principal component Exercise 7: Variance of the PCA features Exercise 8: Intrinsic dimension of the fish data Exercise 9: Dimension reduction with PCA Exercise 10: Dimension reduction of the fish measurements Exercise 11: A tf-idf word-frequency array Exercise 12: Clustering Wikipedia part I Exercise 13: Clustering Wikipedia part II

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Exercise 1: Non-negative matrix factorization (NMF)Exercise 2: Non-negative data Exercise 3: NMF applied to Wikipedia articles Exercise 4: NMF features of the Wikipedia articles Exercise 5: NMF reconstructs samples Exercise 6: NMF learns interpretable parts Exercise 7: NMF learns topics of documents Exercise 8: Explore the LED digits dataset Exercise 9: NMF learns the parts of images Exercise 10: PCA doesn't learn parts Exercise 11: Building recommender systems using NMF Exercise 12: Which articles are similar to 'Cristiano Ronaldo'?Exercise 13: Recommend musical artists part I Exercise 14: Recommend musical artists part II Exercise 15: Final thoughts