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To make predictions with scikit-learn, fit an estimator on training data, then call its predict() method with new rows in the same feature format. For supervised learning, that usually means fitting with a feature matrix X_train and matching targets y_train. The estimator determines what the output means: a classifier predicts labels, while a regressor predicts numeric values.
1. Choose the right kind of estimator
Start by matching the estimator to the question you want answered. Scikit-learn’s estimators share a common fit-oriented API, but they do not all solve the same kind of problem or return the same type of result.
- Classification: predict a category, such as a class label.
- Regression: predict a numeric value.
- Unsupervised learning: identify structure in input data without a target column; these estimators generally do not need
y.
For an overview of the estimator interface and a basic example, see the scikit-learn Getting Started guide.
2. Prepare training data and new rows
For supervised learning, X is typically a two-dimensional feature matrix: each row is one sample, and each column is one feature. The target y contains the answer associated with each row, in the same order. Inputs may be NumPy arrays or other supported array-like structures; some estimators also accept sparse matrices.
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New data must use the features the estimator was trained to expect, in the same order and representation. A row with a missing, extra, or rearranged feature is not equivalent to a correctly structured input.
3. Fit first, then predict
Call fit() with training examples before asking the estimator for predictions. Then pass new feature rows to predict():
from sklearn.ensemble import RandomForestClassifier
X_train = [[1, 2, 3], [11, 12, 13]]
y_train = [0, 1]
model = RandomForestClassifier(random_state=0)
model.fit(X_train, y_train)
X_new = [[4, 5, 6], [14, 15, 16]]
predictions = model.predict(X_new)
print(predictions)This small example illustrates the API only; its tiny dataset says nothing about model quality. Keep the new cases separate from training when the goal is to test performance or generate predictions for cases the model has not seen.
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4. Keep preprocessing consistent with a pipeline
If predictions require preprocessing—such as scaling numeric values or encoding categories—apply the same transformation at training and prediction time. A scikit-learn Pipeline combines transformers and a final estimator behind the familiar fit() and predict() interface. Fitting the pipeline on training data and using it for later predictions helps keep transformations consistent and reduces the risk of information leaking from test data into training.
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In practice, pass raw feature rows in the format expected at the pipeline’s input; the pipeline applies its configured transformations before the final estimator predicts.
5. Understand what prediction methods return
predict(): labels or numeric values
predict(X_new) returns the estimator’s task-specific prediction. For a classifier, that is typically a class label; for a regressor, it is typically a numeric value.
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predict_proba(): optional probability estimates
Some classifiers provide predict_proba(), which returns class probability estimates, but not every classifier implements it. A probability is not automatically reliable just because the method returns a number. For a well-calibrated classifier, cases assigned probability 0.8 should occur in the relevant class about 80% of the time over a sufficiently large group of such cases.
The scikit-learn probability calibration guide explains calibration curves and proper scoring rules such as Brier loss and log loss. Brier loss reflects both calibration and other qualities, including discrimination and uncertainty, so a lower value alone does not establish better calibration. CalibratedClassifierCV can provide calibrated probability outputs for some classifiers that do not natively expose predict_proba().
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Some classifiers expose decision_function() or other optional methods such as predict_log_proba(). A decision score is not synonymous with a probability. Check the selected estimator’s documentation to see which prediction methods it supports and how to interpret their outputs; the scikit-learn glossary describes these API terms.
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6. Evaluate predictions for the problem you have
Producing predictions does not show whether they are useful. Evaluate them on data set aside from fitting, and choose measures based on the task and the consequences of different errors. Classification and regression call for different metrics; when a classifier’s label decisions depend on a threshold, threshold tuning may also matter. Accuracy is not a universal measure of success.
The scikit-learn user guide covers cross-validation, scoring functions, classification metrics, regression metrics, and decision-threshold tuning.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Save a fitted model for later predictions
When predictions need to run in another process or environment, save and load the fitted estimator using a format supported by the model and your deployment needs. The scikit-learn model persistence guide compares ONNX, skops.io, joblib, pickle, and cloudpickle. Support varies by estimator and third-party package. ONNX can enable inference without loading the Python estimator object, but conversion is not available for every scikit-learn or third-party model.
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Python-object formats depend on compatible packages and environment details. Never load a pickle-based artifact from an untrusted source: loading it can execute malicious code. Record the training recipe, a reference to the training data, scikit-learn and dependency versions, and relevant evaluation information so the artifact can be understood and maintained.
Loading a model across scikit-learn versions is not guaranteed. The documentation states: “When an estimator is loaded with a scikit-learn version that is inconsistent with the version the estimator was pickled with, an InconsistentVersionWarning is raised.” The warning signals a version mismatch; it does not make the artifact compatible.
As the scikit-learn developers put it, “Once the trained model is successfully loaded, it can be served to manage different prediction requests.”
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