Binary classification
- Machine Learning course
- Beginner
- Lesson 60/112
- 5 min
Binary classification has two classes: positive and negative.
Binary classification
In two-class problems the classes are usually encoded as 0 (negative) and 1 (positive). "Positive" doesn't mean good or bad; it is the event we are looking for: the disease is present, the transaction is fraud, the customer will leave.
m = LogisticRegression().fit(X, y)
print(m.predict_proba([[5]])) # [[P(0), P(1)]]
print(m.predict([[5]])) # 1 if P(1) > 0.5predict_proba returns two columns; the positive-class probability is taken with [:, 1].
Decision threshold
0.5 is only the default threshold. If missing a patient in cancer screening is costly, the threshold is lowered; more people are flagged positive:
p = m.predict_proba(X_test)[:, 1]
karar = (p >= 0.3).astype(int) # more sensitive thresholdChanging the threshold shifts the balance of decisions without retraining the model; in the evaluation lesson you'll see how this connects to the metrics.
Practice Decide with a threshold
Train a LogisticRegression. Print the positive-class probabilities for X_yeni with np.round(..., 2). Then print the decisions with a 0.3 threshold ((p >= 0.3).astype(int)).
Starter code
import numpy as np
from sklearn.linear_model import LogisticRegression
X = np.array([[1], [2], [3], [4], [5], [6], [7], [8]])
y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
X_yeni = np.array([[3.5], [4.2], [6.0]])
# buraya yazSample solution
import numpy as np
from sklearn.linear_model import LogisticRegression
X = np.array([[1], [2], [3], [4], [5], [6], [7], [8]])
y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
X_yeni = np.array([[3.5], [4.2], [6.0]])
m = LogisticRegression().fit(X, y)
p = m.predict_proba(X_yeni)[:, 1]
print(np.round(p, 2))
print((p >= 0.3).astype(int))Expected output
[0.24 0.41 0.85] [0 1 1]
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 If <code>predict_proba</code> gives <code>[[0.3, 0.7]]</code>, what does <code>predict</code> return?
- [1] Correct answer
- [0]
- [0.7]
- [0.3]
Explanation: The positive-class probability 0.7 > 0.5, so class 1 is chosen.
2 Take the positive class's probability column.
p = m.predict_proba(X_test)[____]p = m.predict_proba(X_test)[:, 1]Explanation: The second column belongs to the second class in classes_, i.e. 1.
3 Match the terms.
| Term | Meaning |
|---|---|
positive class |
The event sought (1) |
negative class |
Absence of the event (0) |
predict_proba[:, 1] |
Positive probability |
threshold |
From probability to decision |