Recall
- Machine Learning course
- Beginner
- Lesson 100/112
- 5 min
Recall shows how many true positives are found.
The ROC curve
As the threshold falls from 1 to 0, recall (the true positive rate, TPR) rises but so does the false positive rate (FPR). The ROC curve plots these two rates for all thresholds:
from sklearn.metrics import roc_curve
fpr, tpr, esikler = roc_curve(y_test, p)
# a (fpr, tpr) point for each thresholdFPR = FP / (FP + TN): the share of true negatives wrongly called positive.
AUC
The area under the ROC curve (AUC) is a one-number summary: 1.0 is perfect, 0.5 random. It is threshold-independent and is interpreted as "the probability that a random positive example scores higher than a random negative".
from sklearn.metrics import roc_auc_score
print(roc_auc_score([0, 0, 1, 1], [0.1, 0.4, 0.35, 0.8])) # 0.75In three of the four positive-negative pairs the positive scored higher: 3/4 = 0.75.
Practice Compute AUC
For the breast cancer test probabilities p, print the roc_auc_score with round(..., 3). Then print how many thresholds roc_curve produced (len(esikler)).
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)
m = make_pipeline(StandardScaler(), LogisticRegression()).fit(X_train, y_train)
p = m.predict_proba(X_test)[:, 1]
from sklearn.metrics import roc_auc_score, roc_curve
# 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)
m = make_pipeline(StandardScaler(), LogisticRegression()).fit(X_train, y_train)
p = m.predict_proba(X_test)[:, 1]
from sklearn.metrics import roc_auc_score, roc_curve
print(round(roc_auc_score(y_test, p), 3))
fpr, tpr, esikler = roc_curve(y_test, p)
print(len(esikler))Expected output
0.995 12
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 What does the ROC curve of a model guessing randomly look like?
- The diagonal from (0,0) to (1,1) Correct answer
- Hugging the top-left corner
- A horizontal line
- A single point
Explanation: For a random model TPR always equals FPR; its AUC is 0.5.
2 Compute the area under the ROC curve.
auc = ____(y_test, p)auc = roc_auc_score(y_test, p)Explanation: roc_auc_score needs probabilities or scores, not decisions.
3 Match the terms.
| Term | Meaning |
|---|---|
TPR |
Recall |
FPR |
FP / (FP + TN) |
AUC = 0.5 |
Random |
AUC = 1.0 |
Perfect ranking |