Vissengegevens schalen voor clustering

Je krijgt een array samples met metingen van vissen. Elke rij stelt een individuele vis voor. De metingen, zoals gewicht in gram, lengte in centimeter en de procentuele verhouding van hoogte tot lengte, hebben heel verschillende schalen. Om deze data effectief te clusteren, moet je deze features eerst standaardiseren. In deze oefening bouw je een pipeline om de data te standaardiseren en te clusteren.

Deze vismetingen zijn afkomstig uit de Journal of Statistics Education.

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

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Oefeninstructies

Importeer:
- make_pipeline uit sklearn.pipeline.
- StandardScaler uit sklearn.preprocessing.
- KMeans uit sklearn.cluster.
Maak een instantie van StandardScaler met de naam scaler.
Maak een instantie van KMeans met 4 clusters met de naam kmeans.
Maak een pipeline met de naam pipeline die scaler en kmeans aan elkaar koppelt. Dit doe je door ze als argumenten door te geven aan make_pipeline().

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Perform the necessary imports
from ____ import ____
from ____ import ____
from ____ import ____

# Create scaler: scaler
scaler = ____

# Create KMeans instance: kmeans
kmeans = ____

# Create pipeline: pipeline
pipeline = ____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

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Begin de cursus 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: Hoeveel clusters?Exercise 3: Clusteren van 2D-punten Exercise 4: Bekijk je clustering Exercise 5: Een clustering evalueren Exercise 6: Hoeveel graanclusters?Exercise 7: De graanclustering evalueren Exercise 8: Features transformeren voor betere clusterings Exercise 9: Vissengegevens schalen voor clustering

Huidige oefening

Exercise 10: Clusteren van de visgegevens Exercise 11: Aandelen clusteren met KMeans Exercise 12: Welke aandelen bewegen samen?

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