The scikit-learn API: fit, transform, predict
Understand the estimator interface and the train/test split workflow.
The scikit-learn API: fit, transform, predict is a free Python Academy lesson on CoddyKit. This is lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Python Academy learning path, and your progress syncs across the web and the CoddyKit app. The Python Academy course includes 4 lessons in total.
What Is scikit-learn?
scikit-learn is the go-to Python library for classical machine learning. It provides a consistent API: every estimator has fit(), and most have predict() or transform().
from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array([[1],[2],[3],[4],[5]])
y = np.array([2, 4, 6, 8, 10])
model = LinearRegression().fit(X, y)
print(model.predict([[6]])) # [12.]Train/Test Split
Always split data before training to evaluate how well the model generalises to unseen data.
from sklearn.model_selection import train_test_split
import numpy as np
X = np.random.rand(100, 5)
y = (X[:,0] > 0.5).astype(int)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
print(X_train.shape, X_test.shape) # (80,5) (20,5)fit() — Training
estimator.fit(X, y) trains the model on X (features) and y (labels). For unsupervised methods, only X is passed.
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=200, random_state=0)
model = LogisticRegression()
model.fit(X, y)
print("Trained:", model.classes_)predict() and predict_proba()
predict(X) returns class labels; predict_proba(X) returns class probabilities for classifiers.
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
X, y = make_classification(random_state=0)
model = LogisticRegression().fit(X, y)
print(model.predict(X[:5])) # [0 1 0 ...]
print(model.predict_proba(X[:2])) # [[0.7 0.3] ...]Transformers: fit and transform
Transformers (scalers, encoders) have fit(X) + transform(X). Always fit on training data only, then transform both train and test.
from sklearn.preprocessing import StandardScaler
import numpy as np
X_train = np.array([[1,2],[3,4],[5,6]])
X_test = np.array([[2,3],[4,5]])
scaler = StandardScaler()
X_train_s = scaler.fit_transform(X_train) # fit + transform
X_test_s = scaler.transform(X_test) # transform only (using train stats)Pipeline
A Pipeline chains transformers and an estimator into a single object. Prevents data leakage by applying transformations within cross-validation correctly.
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
pipe = Pipeline([
("scaler", StandardScaler()),
("clf", SVC())
])
pipe.fit(X_train, y_train)
print(pipe.score(X_test, y_test))Accuracy and Other Metrics
Use accuracy_score, f1_score, classification_report to evaluate classifiers.
from sklearn.metrics import accuracy_score, classification_report
y_pred = model.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))Cross-Validation
cross_val_score evaluates a model using k-fold cross-validation without manually splitting data.
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
X, y = make_classification(random_state=0)
scores = cross_val_score(LogisticRegression(), X, y, cv=5)
print(f"CV scores: {scores.mean():.3f} ± {scores.std():.3f}")Hyperparameter Tuning with GridSearchCV
GridSearchCV exhaustively searches a parameter grid using cross-validation to find the best hyperparameters.
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.datasets import make_classification
X, y = make_classification(random_state=0)
param_grid = {"C": [0.1, 1, 10], "kernel": ["rbf", "linear"]}
gs = GridSearchCV(SVC(), param_grid, cv=5)
gs.fit(X, y)
print(gs.best_params_, gs.best_score_)Preprocessing: LabelEncoder and OneHotEncoder
Encode categorical labels with LabelEncoder and categorical features with OneHotEncoder or ColumnTransformer.
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import numpy as np
le = LabelEncoder()
print(le.fit_transform(["cat","dog","cat","fish"])) # [0 1 0 2]
ohe = OneHotEncoder(sparse_output=False)
print(ohe.fit_transform([["red"],["green"],["blue"]]))Saving and Loading Models
Persist trained models with joblib (faster than pickle for NumPy arrays).
import joblib
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
X, y = make_classification(random_state=0)
model = LogisticRegression().fit(X, y)
joblib.dump(model, "model.joblib")
loaded = joblib.load("model.joblib")
print(loaded.predict(X[:3]))Quick Check
Why should you call scaler.fit() only on training data and not on test data?
Recap
scikit-learn's uniform API: fit() trains, predict() infers, transform() pre-processes. Use Pipeline to chain steps. Split data with train_test_split, evaluate with cross_val_score, and tune with GridSearchCV.
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Is the “The scikit-learn API: fit, transform, predict” lesson free?
Yes — the full text of “The scikit-learn API: fit, transform, predict” is free to read here on the web. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Python Academy course, upgrade to CoddyKit PRO. The Python Academy course includes 4 lessons in total.
What will I learn in “The scikit-learn API: fit, transform, predict”?
Understand the estimator interface and the train/test split workflow. You practise Python Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Python Academy?
No prior experience is required. Python Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 1 of 4.
How long does the “The scikit-learn API: fit, transform, predict” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
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All lessons in this course
- The scikit-learn API: fit, transform, predict
- Linear Models: Regression and Classification
- Tree-Based Models: Decision Trees and Random Forests
- Model Evaluation and Cross-Validation