3. Classification

Classification Metrics

Consider a binary classification problem, with 1 representing positive, and 0 representing negative class. A true (false) positive is an outcome where the model correctly (incorrectly) predicts the positive class. Similarly, a true (false) negative is an outcome where the model correctly (incorrectly) predicts the negative class.

FP (false positive) and FN (false negative) are often called Type I Error and Type II Error. We will get into details when we discuss Confusion matrix (a special type of contingency table).

Note: I will mainly discuss binary classification problems as multi-class problems are generalizations of binary ones.

While binary classifiers distinguish between two classes, multiclass (also called multinomial) classifiers can distinguish between more than two classes. Some algorithms (such as SGD, Random Forest, and Naive Bayes classifiers) are capable of handling multiple classes natively. Others (such as Logistic Regression or Support Vector Machine classifiers) are strictly binary classifiers. However, there are various strategies that you can use to perform multiclass classification with multiple binary classifiers. The two main approaches for multi-class classification problems that utilize binary classification are: OvR and OvO.

  • One vs Rest (OvR): It focuses on only one class at a time and the rest of the classes are called other. It is also called the one-versus-all (OvA) method.

Example: MNIST dataset(classes 0-9): 0 - classifier, 1 - classifier, ..., 9 - classifier.

One way to create a system that can classify the digit images into 10 classes (from 0 to 9) is to train 10 binary classifiers (time complexity ∼O(N)\sim \mathcal{O}(N)), one for each digit (a 0-detector, a 1-detector, a 2- detector, and so on). Then when you want to classify an image, you get the decision score from each classifier for that image and you select the class whose classifier outputs the highest score.

  • One vs One (OvO): Trains a binary classifier for every pair of classes, in total N×(N−1)2\dfrac{N × (N-1)}{2} binary classification problems for N number of classes.

Example: MNIST dataset (classes 0-9): 0 vs 1,..., 0 vs 9, 1 vs 2, ..., 1 vs 9, ... 8 vs 9

The main advantage of OvO is that each classifier will be trained only on the part of training set where two classes appear. So it is much faster and requires less memory than OvR, but the number of classifiers to be trained grows fast (time complexity ∼O(N2)\sim \mathcal{O}(N^2)). Support vector machines, for example, scale poorly with the size of the training set; hence, OvO is preferred and it is faster and the training sets are smaller.

Accuracy: The most naive approach to measuring a model's performance in a binary classification problem is calculating the ratio of correct predictions, which is called accuracy.

Accuracy=# Correct Predictions#   Total   Predictions=TP+TNTP+FP+TN+FN\textbf{Accuracy} = \dfrac{\text{\# Correct Predictions}}{\text{\# \ \ Total \ \ Predictions}} = \dfrac{TP + TN}{TP+FP+TN+FN}

Caution: Accuracy alone doesn't give enough information about the model's performance, as in class-imbalanced datasets, (such as in anomaly detection problems) there are significant disparity between the number of positive and negative labels. Consider a simple model that predicts all of the instances as normal, since the percentage of anomalies is much smaller than normal instances this naive model would yield a really high accuracy score, which is misleading.

Example: Consider a fraud detection (classification) problem, in which the percentage of fraudulent transactions is less than ≤1%\leq 1\% , then a naive model predicting every transaction as non-fraudulent would yield an accuracy ≥99%≥ 99 \%, which is overly optimistic for different datasets where the data is more balanced.

Confusion Matrix

There are other metrics that are more useful for different types of problems, and more reliable than accuracy in general. Some of them are:

Precision=TPTP+FP (PPV := Positive Predictive Value)\textbf{Precision} = \dfrac{TP}{TP + FP} \ \text{(PPV := Positive Predictive Value)}

 TNTN+FN (NPV := Negative Predictive Value)\quad \quad \quad \quad \quad \ \dfrac{TN}{TN + FN} \ \text{(NPV := Negative Predictive Value)}

Recall (Sensitivity)=TPTP+FN (TPR := True Positive Rate)\textbf{Recall (Sensitivity)} = \dfrac{TP}{TP + FN} \ \text{(TPR := True Positive Rate)}

Specificity=TNTN+FP=1−FPFP+TN =1 - FPR\textbf{Specificity} = \dfrac{TN}{TN + FP} =1 - \dfrac{FP}{FP + TN} \ =\text{1 - FPR}

Notice, similarly we can define FPR (False Positive Rate)=1−Specificity.\text{FPR (False Positive Rate)} = 1 - \textbf{Specificity}.These three metrics are very important as it is a common practice to visualize the scatter plot of Precision vs Recall and TPR (Recall, Sensitivity) vs FPR (1-Specificity), which is also called ROC (Receiver Operating Characteristic) curve, and calculating the area under the ROC curve, called AUC, in order to evaluate model's predictive power.

It is also often convenient to combine precision and recall into a single metric called the F1-Score, in particular, if you need a simple way to compare two classifiers. The F1-score is the harmonic mean of precision and recall. Whereas the regular mean treats all values equally, the harmonic mean gives much more weight to low values. As a result, the classifier will only get a high F1-score if both recall and precision are high.

F1-Score=2∗Precision∗RecallPrecision+Precision=21Precision+1Recall=HM(P, R)\textbf{F1-Score} = \dfrac{2 * \text{Precision} * \text{Recall}}{\text{Precision} + \text{Precision}} = \dfrac{2}{\dfrac{1}{\text{Precision}} + \dfrac{1}{\text{Recall}}} = \textbf{HM(\text{P}, \text{R})}

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