Demystifying the Confusion Matrix: Unveiling Your 5-Detector's Performance (Part 4)
Greetings, data science adventurers! In Part 3, we exposed the limitations of relying solely on accuracy. We discovered that a simple "dummy classifier" could achieve impressive accuracy on our imbalanced MNIST dataset by always predicting the most frequent class (non-5s). While this approach inflates the accuracy metric, it offers little practical value.
This is where the confusion matrix emerges as a powerful tool for gaining a deeper understanding of your 5-detector's performance. Let's embark on a journey to unveil its secrets!
Understanding the Confusion Matrix:
Imagine a battlefield where your 5-detector confronts images. The confusion matrix tallies the outcomes of these battles, revealing how often the model confuses one class for another. For instance, to determine how many 6s were mistakenly classified as 0s, you'd consult row #6, column #0 of the matrix.
Building the Confusion Matrix:
To construct the confusion matrix, we need predictions to compare against actual targets. While the test set is tempting, it's best to reserve it for final evaluation. Instead, we can leverage cross_val_predict() from Scikit-Learn:
Python
from sklearn.model_selection import cross_val_predict
y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)
Similar to cross_val_score(), this function performs k-fold cross-validation, but instead of scores, it returns predictions for each test fold. This provides "clean" (out-of-sample) predictions for the training set, meaning the model predicts on data it never saw during training.
Now, we're ready to construct the confusion matrix using confusion_matrix():
Python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_train_5, y_train_pred)
Decoding the Matrix:
Each row in the confusion matrix represents an actual class, and each column represents a predicted class. Remember that we are working with a binary classifier, which means there are only two classes: "5" and "not 5". Let's analyze our example matrix:
>>> cm
array([[53892, 687],
[ 1891, 3530]])
A perfect classifier would only have true positives and true negatives, resulting in a non-zero diagonal (top left to bottom right). Here's an example of a perfect confusion matrix:
Python
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y_train_perfect_predictions = y_train_5
confusion_matrix(y_train_5, y_train_perfect_predictions)
array([[54579, 0],
[ 0, 5421]])
In this case, the model:
The confusion matrix provides a wealth of information about the model's performance, but sometimes more concise metrics are needed.
Beyond the Confusion Matrix: Precision and Recall
The confusion matrix offers valuable insights, but sometimes a more concise metric is desired. Two key metrics derived from the matrix are precision and recall:
precision = TP / (TP + FP)
TP is the number of true positives, and FP is the number of false positives.
A high precision indicates that most of the model's positive predictions are indeed correct. However, a classifier that simply predicts "non-5" for all images would have perfect precision (dividing 1 by 0), but it wouldn't be very useful.
recall = TP / (TP + FN)
FN is, of course, the number of false negatives.
A high recall suggests the model effectively captures most of the positive examples.
The interplay between precision and recall is crucial in classifier evaluation. In the next part of this series, we'll dive even deeper into the concepts of precision and recall.
References:
Géron, Aurélien. Hands-on Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems. 2nd ed., O’Reilly Media, Inc., Sept. 2019.
Kundu, Rohit. “Confusion Matrix: How to Use It & Interpret Results [Examples].” Www.v7labs.com, 13 Sept. 2022, www.v7labs.com/blog/confusion-matrix-guide.
#machinelearning #ML #classification #imageclassification #mnist #beginners #tutorial #datascience #artificialintelligence #AI #scikitlearn #python
Understanding the confusion matrix is a game-changer in the world of machine learning! It's great to see an article breaking it down into simple terms. Key metrics like precision and recall can really make a difference in fine-tuning models for optimal performance. Looking forward to diving into this article and mastering these concepts!