Predicting Probabilities with SGDClassifier in Scikit-Learn

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When working with machine learning models, sometimes we don’t just want to know the predicted class (e.g., cat vs dog). Instead, we want the probability distribution over all possible classes.

For example, instead of:

  • "This is a cat 🐱"

We might want:

  • "This is a cat with 90% probability and a dog with 10% probability."

This is where the predict_proba method comes into play.

The Question

Which of the following methods is used to find the predicted probability of each class of training samples using a trained model = SGDClassifier()?

Options:

  1. ✅ model.predict_proba(X_train)

  2. ❌ model.predict(X_train)

  3. ❌ model.estimate_

  4. ❌ model.predict_proba_

Explanation of Each Option

1. ✅ model.predict_proba(X_train)

from sklearn.linear_model import SGDClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load dataset
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# Train model with probability support
clf = SGDClassifier(loss="log_loss", random_state=42)  # use logistic regression
clf.fit(X_train, y_train)

# Get probability predictions
probs = clf.predict_proba(X_test[:5])
print(probs)

Output (example):

[[0.85 0.10 0.05],
 [0.02 0.70 0.28],
 [0.01 0.05 0.94],
 ...]

Here each row shows the probability distribution across classes.

2. ❌ model.predict(X_train)

  • Returns the predicted class labels (hard classification).

  • Example: [0, 1, 2, ...]

  • Does not give probabilities.

3. ❌ model.estimate_

  • Not a valid method in SGDClassifier.

4. ❌ model.predict_proba_

  • This looks like an attribute, but it doesn’t exist in SGDClassifier.

  • The correct function is model.predict_proba(X) (with parentheses).

⚠️ Important Note about SGDClassifier

By default, SGDClassifier does not support probability prediction unless you set:

SGDClassifier(loss="log_loss")

  • With loss="hinge" (SVM-like loss), you can only use decision_function() (not probabilities).

  • With loss="log_loss", it uses logistic regression, so predict_proba becomes available.

✅ Final Answer

The correct method is:

model.predict_proba(X_train)

Would you like me to also add a comparison between predict_proba vs decision_function (since many people confuse them with SGDClassifier)?

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