Classification Metrics

While it’s important to know classification algorithms, it’s equally vital to evaluate them correctly. Understanding key classification evaluation metrics is crucial when answering the fundamental question of whether a model is any good. These metrics help in making informed decisions about model selection and performance assessments.

Classification metrics

Classification metrics play a crucial role in the realm of data analysis and ML, providing insights into the performance of predictive models aimed at discrete categorical outcomes. In this section, we’ll delve into a variety of evaluation techniques and metrics essential for assessing the accuracy and effectiveness of classification models. These metrics are instrumental in understanding the quality of our predictions and guiding decisions concerning model choice, tuning, and deployment.

The primary classification metrics we’ll discuss include precision, recall, F1 score, and the receiver operating characteristic (ROC) curve. Each of these metrics serves a distinct purpose in evaluating classification model performance. Precision and recall offer insights into the model’s ability to make accurate positive predictions and find all positive instances, respectively. The F1 score combines these metrics into a single value, while the ROC curve assesses the model’s trade-off between true positive and false positive rates.

This section will provide a comprehensive understanding of these metrics, highlighting their strengths, limitations, and ideal applications.

Confusion matrix

A confusion matrix is a table that describes the performance of a classification model. It shows the number of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions made by the model. It’s usually presented in the following format:

Ideally, we want all values to fall under true positive and true negative. In other words, we want all our predictions to be correct. Of course, in practice, this is nearly impossible, but classification metrics provide us with a measure of how close we are to this ideal:

• Accuracy measures the percentage of correct predictions made by the model, ranging between 0 and 1.

• Precision measures the percentage of positive predictions made by the model that are actually true positives, ranging between 0 and 1.

• Recall measures the percentage of true positive cases that are correctly predicted by the model. It’s also called sensitivity or true positive rate (TPR), and it ranges between 0 and 1.

• F1 score is the harmonic mean of precision and recall. It provides a balance between these two metrics and is a good overall measure of the model’s performance. It ranges between 0 and 1.

To better understand how these metrics are calculated, let’s see a short example using actual figures:

We can read the table like this: out of $100$ actual positives, we correctly predict $98$. Now let’s see how the previous metrics would be calculated here:

• Accuracy = $\frac{(98+80)}{(98+80+20+2)}=\frac{178}{200}=0.89$ ...