Confusion Matrix: Model Selection in Machine Learning
Life is really simple, but we insist on making it complicated - Confucius
But don't worry confusion matrix will not be more complicated as he said!
We previously explored the concept of cross-validation, as we knew that, CV is used to find Which machine learning model can be used for the specific case but how? How are we going to compare CV among models? Here comes the Hero, Confusion Matrix.
Confusion Matrix: A Bird's-Eye View
Despite its name, the confusion matrix offers valuable insights into model performance. It's a square table with dimensions (n x n), where n represents the number of classes your model predicts. Let's delve deeper into a classic scenario: diagnosing heart disease (positive class) or its absence (negative class).
The Heart of the Matter: Decoding the Confusion Matrix
Imagine a 2x2 confusion matrix for our heart disease example:
In this 2x2 the,
1x1 (top left corner) --> True positive (The actual number of people has heart disease is correctly identified by the machine)
1x2 (top right corner) --> False positive (The actual number of people has no heart disease but the machine says that they have heart disease)
2x1 (bottom left corner) --> False Negative (The actual number of people has heart disease but identified by the machine that they have no heart disease)
2x2 (bottom right corner) --> True Negative (The actual number of people has no heart disease is correctly identified by the machine that they have no heart disease)
The Decisive Round: Choosing the Champion Model
Let's say we've applied two models, K-Nearest Neighbors (KNN) and Random Forest, to diagnose heart disease. We'll obtain confusion matrices for both, allowing us to compare their performance. Visualizing these matrices (which you can include as separate figures) will help identify the model with a higher concentration of values on the diagonal (TP and TN). This indicates a model's proficiency in correctly classifying both positive and negative cases.
Now let's compare these two model's performance by looking at their confusion matrix for this heart disease case.
While we look at our naked eyes, we can confirm that the random forest performs well than KNN so, we can directly choose Random forest to predict heart disease
But what if we have another model which performs quite similar to random forest? at that time we will go for some more detailed view
Beyond the Matrix: Unveiling Sensitivity and Specificity
While the confusion matrix provides a visual snapshot, we can delve deeper using metrics like sensitivity and specificity:
Now, we have two new weapons Sensitivity and Specificity in our hands, lets perform these two metrics on our new data with the new confusion matrix of Logistic regression
Now substitue the corresponding values and do the math,
For Logistic Regression,
Sensitivity = TP / TP + FN = 139 / 139+32 = 0.81 *100 = 81%
Specificity = TN/ TN + FP = 112/112+20 = 0.84*100 = 84%
Similarly for Random forest,
Sensitivity = 83%
Specificity = 83%
The Final Verdict: A Nuanced Approach
This highlights a crucial point. Model selection isn't always a clear-cut choice. It depends on the specific problem and priorities. If accurately detecting heart disease is paramount, Random Forest might be preferable. However, if minimizing false positives is crucial, Logistic Regression could be a better fit.
In Conclusion: The Power of Informed Choice
The confusion matrix, coupled with sensitivity and specificity, empowers us to make informed decisions when selecting the champion model for a specific machine-learning task. By carefully evaluating these metrics, we can ensure our models deliver the optimal results for the intended purpose.
Let's meet next week!
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