How to Use Matthews Correlation Coefficient in SciPy for Better Classifier Evaluation

Matthews Correlation Coefficient (MCC) is a popular metric used in the field of machine learning to evaluate the performance of classification models. It takes into account both true positive and true negative predictions and is particularly useful when dealing with imbalanced datasets. In this article, we will discuss how to use MCC in SciPy for better classifier evaluation.

To begin with, let’s first understand what Matthews Correlation Coefficient is. MCC is calculated using the formula:

MCC = (TP * TN – FP * FN) / √((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))

Where:
TP = True Positives
TN = True Negatives
FP = False Positives
FN = False Negatives

A MCC of 1 indicates a perfect classification, -1 indicates a completely wrong classification, and 0 indicates a random classification.

Now, let’s move on to how to calculate MCC using SciPy. SciPy is a popular Python library used for scientific computing and includes functions for calculating MCC along with other evaluation metrics for classifiers.

To calculate MCC using SciPy, first, you need to import the necessary functions from the `scipy.stats` module:

“`python
from scipy.stats import matthews_corrcoef
“`

Next, you need to provide the true labels and predicted labels from your model to the `matthews_corrcoef` function:

“`python
true_labels = [0, 1, 1, 0, 1] predicted_labels = [0, 1, 0, 0, 1]

mcc = matthews_corrcoef(true_labels, predicted_labels)
print(“Matthews Correlation Coefficient:”, mcc)
“`

By running this code, you will get the MCC value for your classifier’s predictions. You can use this value to evaluate the performance of your model.

It is important to note that MCC is particularly useful when dealing with imbalanced datasets, as it considers both positive and negative samples and is not affected by the class distribution in the dataset.

In conclusion, Matthews Correlation Coefficient is a valuable metric for evaluating classification models, especially in scenarios where datasets are imbalanced. By using SciPy’s `matthews_corrcoef` function, you can easily calculate MCC and assess the performance of your classifier. Incorporating MCC into your model evaluation process can help you make more informed decisions and improve the overall quality of your machine learning models.