Entropy Calculation for Binary Classification using SciPy
In the code, we define the target variable y by converting multi-classification into a binary classification tasks for simplicity.
- Here, we will use iris dataset and classify Setosa (class 0) vs. Non-Setosa (class 1) species.
- Using
np.bincount, we compute the frequency of each unique value in the target variable, which essentially gives us the counts of class 0 and class 1. - Finally, we pass these counts to the
entropyfunction along with the base of 2 to compute the entropy of the target variable.
from sklearn import datasets
from scipy.stats import entropy
import numpy as np
iris = datasets.load_iris()
X = iris.data
# For simplicity, we'll classify the Iris species into two classes: Setosa (class 0) and Non-Setosa (class 1)
y = (iris.target != 0).astype(int) # Setosa (class 0) vs Non-Setosa (class 1)
y_entropy = entropy(np.bincount(y), base=2) # Compute the entropy of the target variable (y)
print("Entropy of Iris dataset (binary classification):", y_entropy)
Output:
Entropy of Iris dataset (binary classification): 0.9182958340544894
How to Compute Entropy using SciPy?
Entropy is a fundamental concept in measuring the uncertainty or randomness in a dataset. Entropy plays a very significant role in machine learning models such as decision trees, helping to decide how best to partition input at each node. Even for those who are not very knowledgeable in the underlying mathematics, the Scipy library for Python, provides features that make computing entropy simple.
In this post, we will understand how to compute entropy using Popular python’s library scipy.
How to Compute Entropy using SciPy?
- What is Entropy?
- Why Compute Entropy?
- Calculating Entropy with SciPy
- Entropy Calculation for Binary Classification using Scipy
- Entropy Calculation for Multi-Class Classification using Scipy
- Conclusion
- How to Compute Entropy using SciPy?- FAQs