Confusion matrix & f1-score

Last modified on 01 Oct 2021.

Confusion matrix

How to remember?

• True/False indicates what we predicted is right/wrong.
• Positive/Negative is what we predicted (yes or no).

Type I / Type II errors

• FP = Type I error = rejection of true null hypothesis = negative results are predicted wrongly = what we predict positive is actually negative.
• FN = Type II error = non-rejection of a false null hypothesis = positive results are predicted wrongly = what we predict negative are actually positive.

Why CM is important?

Give a general view about our model, “is it really good?” thanks to precision and recall!

Precision & Recall

• Precision: How many of our positive predictions are really true? (Check the accuracy of our positive predictions).

  $\mathrm {precision} = \dfrac{\mathrm{true\, positive}}{\mathrm{positively\, predicted\, results}} = \dfrac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}}.$

• Recall: How many of positive results belong to our predictions? (Do we miss some negative predictions?)

  $\mathrm {recall} = \dfrac{\mathrm{true\, positive}}{\mathrm{positively\, actual\, results}} = \dfrac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}.$

When to use?

• Precision is importantly used when the “wrongly predicted yes” (FP) influences much (e.g. This email is spam? – results yes but actually no and we lost important emails!).
• Recall is importantly used when the “wrongly predicted no” (FN) influences much (e.g. In the banking industry, this transaction is fraudulent? – results no but actually yes and we lost money!).

F1-Score

High precision and low recall or vice versa? F1-Score gives us a balance between precision and recall.

$f_1 = \left({\frac {\mathrm {recall} ^{-1}+\mathrm {precision} ^{-1}}{2}}\right)^{-1}=2\times {\frac {\mathrm {precision} \cdot \mathrm {recall} }{\mathrm {precision} +\mathrm {recall} }}.$

F1-score depends on how we label the class “positive”. This email is spam? is very different from This email is not spam?

When to use F1-Score?

• When you need a balance between precision and recall.
• When we have a “skewed class” problem (uneven class distribution, too many “yes” and very few “no”, for example).
• One of precision and recall is improved but the other changes too much, then f1-score will be very small!

How to choose f1-score value?

Normally, $f_1\in (0,1]$ and it gets the higher values, the better our model is.

• The best one ($f_1=1$), both precision and recall get $100\%$.
• One of precision and recall gets very small value (close to 0), $f_1$ is very small, our model is not good!

What if we prefer one of precision and recall than the other? We consider $f_{\beta}$^[ref]

$f_{\beta} = ( 1 + \beta^2)\frac{\text{precision}\cdot\text{recall}}{\beta^2\cdot\text{precision} + \text{recall}}$

$f_1$ is a special case of $f_{\beta}$ when $\beta=1$:

• When precision is more important than recall, we choose $\beta < 1$ (usually choose $\beta=0.5$).
• When recall is more important than precision, we choose $\beta > 1$ (usually choose $\beta=2$).

Accuracy / Specificity

• Accuracy: How accurate our predictions to the whole predictions?

  $\mathrm{accuracy} = \dfrac{TP + TN}{TP + TN + FP + FN}$

• Specificity: How many negative results belong to our predictions?

  $\mathrm{specificity} = \dfrac{TN}{FP + TN}$

When to use?

• Accuaracy is used when we have symmetric datasets.
• Specificity is used when we care about TN values and don’t want to make false alarms of the FP values (e.g. drug test).

Confusion Matrix & F1-Score with Scikit-learn

from sklearn.metrics import confusion_matrix
n_classes = target.shape[0]
confusion_matrix(y_true, y_pred, labels=range(n_classes))

Precision / Reacall / f1-score / support

from sklearn.metrics import classification_report
classification_report(y_test, y_pred)

ROC curve,

from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
%matplotlib inline

fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)
# create plot
plt.plot(fpr, tpr, label='ROC curve')
plt.plot([0, 1], [0, 1], 'k--', label='Random guess')
_ = plt.xlabel('False Positive Rate')
_ = plt.ylabel('True Positive Rate')
_ = plt.title('ROC Curve')
_ = plt.xlim([-0.02, 1])
_ = plt.ylim([0, 1.02])
_ = plt.legend(loc="lower right")

References

1. Classification: Precision and Recall - Google Developers, Machine Learning Crash Course.
2. Classification: Check Your Understanding (Accuracy, Precision, Recall) - Google Developers, Machine Learning Crash Course.
3. F-measure versus Accuracy - NLP blog.
4. Accuracy, Precision, Recall or F1? - Koo Ping Shung, Towards Data Science.
5. Dealing with Imbalanced data: undersampling, oversampling and proper cross-validation - Marco Altini.
6. Accuracy, Recall, Precision, F-Score & Specificity, which to optimize on? - Salma Ghoneim, Towards Data Science.

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