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homogeneity_score using sklearn in Python
• Last Updated : 01 Oct, 2020

An entirely homogeneous clustering is one where each cluster has information that directs a place toward a similar class label. Homogeneity portrays the closeness of the clustering algorithm to this (homogeneity_score) perfection.

This metric is autonomous of the outright values of the labels. A permutation of the cluster label values won’t change the score value in any way.

Syntax : sklearn.metrics.homogeneity_score(labels_true, labels_pred)

The Metric is not symmetric, switching label_true with label_pred will return the completeness_score.

Parameters :

• labels_true:<int array, shape = [n_samples]> : It accept the ground truth class labels to be used as a reference.
• labels_pred: <array-like of shape (n_samples,)>: It accepts the cluster labels to evaluate.

Returns:

homogeneity:<float>: Its return the score between 0.0 and 1.0 stands for perfectly homogeneous labeling.

Example1:

## Python3

 `import` `pandas as pd``import` `matplotlib.pyplot as plt``from` `sklearn.cluster ``import` `KMeans``from` `sklearn.metrics ``import` `homogeneity_score`` ` ` ` `# Changing the location file``# cd C:\Users\Dev\Desktop\Credit Card Fraud`` ` `# Loading the data``df ``=` `pd.read_csv(``'creditcard.csv'``)`` ` `# Separating the dependent and independent variables``y ``=` `df[``'Class'``]``X ``=` `df.drop(``'Class'``, axis``=``1``)`` ` `# Building the clustering model``kmeans ``=` `KMeans(n_clusters``=``2``)`` ` `# Training the clustering model``kmeans.fit(X)`` ` `# Storing the predicted Clustering labels``labels ``=` `kmeans.predict(X)`` ` `# Evaluating the performance``homogeneity_score(y, labels)`

Output:

```0.00496764949717645
```

Example 2: Perfectly homogeneous:

## Python3

 `from` `sklearn.metrics.cluster ``import` `homogeneity_score`` ` `# Evaluate the score``hscore ``=` `homogeneity_score([``0``, ``1``, ``0``, ``1``], [``1``, ``0``, ``1``, ``0``])`` ` `print``(hscore)`

Output:

```1.0
```

Example 3: Non-perfect labelings that further split classes into more clusters can be perfectly homogeneous:

## Python3

 `from` `sklearn.metrics.cluster ``import` `homogeneity_score`` ` `# Evaluate the score``hscore ``=` `homogeneity_score([``0``, ``0``, ``1``, ``1``], [``0``, ``1``, ``2``, ``3``])`` ` `print``(hscore)`

Output:

```0.9999999999999999
```

Example 4: Include samples from different classes don’t make for homogeneous labeling:

## Python3

 `from` `sklearn.metrics.cluster ``import` `homogeneity_score`` ` `# Evaluate the score``hscore ``=` `homogeneity_score([``0``, ``0``, ``1``, ``1``], [``0``, ``1``, ``0``, ``1``])`` ` `print``(hscore)`

Output:

```0.0
```

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