Confusion matrix
- Machine Learning course
- Beginner
- Lesson 63/112
- 5 min
A confusion matrix shows classification results in detail.
Confusion matrix
The confusion matrix shows which class was confused with which. In a binary problem there are four cells:
from sklearn.metrics import confusion_matrix
y_true = [1, 0, 1, 1, 0, 0]
y_pred = [1, 0, 0, 1, 1, 0]
print(confusion_matrix(y_true, y_pred))
# [[2 1] row: true 0 -> TN=2, FP=1
# [1 2]] row: true 1 -> FN=1, TP=2Rows are the true classes and columns the predictions. The diagonal counts correct predictions, the off-diagonal counts errors.
Reading the cells
With ravel() the four values can be unpacked in order. Which error is costlier depends on the problem:
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
# spam filter: FP (important e-mail went to spam) is costly
# cancer screening: FN (patient missed) is costlyIn a multi-class problem the matrix becomes k×k; large off-diagonal values show which classes the model can't separate.
Practice Get the matrix
Get test predictions on the breast cancer data with a scaled logistic regression pipeline. Print the confusion matrix, then unpack it with ravel() and print the FN and FP counts separated by a space.
Starter code
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0, stratify=y)
from sklearn.metrics import confusion_matrix
m = make_pipeline(StandardScaler(), LogisticRegression()).fit(X_train, y_train)
# buraya yazSample solution
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0, stratify=y)
from sklearn.metrics import confusion_matrix
m = make_pipeline(StandardScaler(), LogisticRegression()).fit(X_train, y_train)
cm = confusion_matrix(y_test, m.predict(X_test))
print(cm)
tn, fp, fn, tp = cm.ravel()
print(fn, fp)Expected output
[[50 3] [ 3 87]] 3 3
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 Calling a sick person "healthy" falls into which cell?
- True positive (TP)
- False negative (FN) Correct answer
- False positive (FP)
- True negative (TN)
Explanation: Truly positive (sick), predicted negative (healthy): a false negative.
2 Unpack the matrix into four values.
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).____()tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()Explanation: ravel flattens the matrix in row order: TN, FP, FN, TP.
3 Match the cells.
| Term | Meaning |
|---|---|
TP |
Positive, predicted right |
TN |
Negative, predicted right |
FP |
False alarm |
FN |
Missed positive |