Scikit-Learn Workflow & Pipeline Lab

1. Prepare X, y = load()

2. Split train_test_split()

3. Instantiate model = Estimator()

4. Fit model.fit(X_tr, y_tr)

5. Predict & Eval model.predict(X_te)

Training Data (45)

Testing / Unseen Data (15)

Fitted Model Curve (LinearRegression)

Active Inference Probe

Click anywhere on canvas to run model.predict()

Live Inference Probe: X = 2,100 sq ft model.predict()

Predicted Label (Price): $412,500.00

Evaluated using current model parameters learned exclusively from training samples.

Training Performance

28,450.2

Train RMSE (Lower is better)

Test / Generalization

31,120.8

Test RMSE (True test of model)

Verified Scikit-Learn Python Code

Syncs automatically with UI

# Loading code...

Workflow Configuration

Dataset & Problem Type

Estimator Algorithm

Test Split Ratio (test_size) 25%

10% (More Train) 60% (More Test)

Data Leakage Counterfactual

Introduce Data Leakage

Fit model on X_all instead of X_train

Test Input (Square Feet) 2100 sq ft

Key Scikit-learn Rules from the Guide

1. The fit() Paradigm:
Learning occurs strictly in fit(X_train, y_train). The model calculates weights without peeking at the test split.

2. The predict() Paradigm:
predict(X_test) transforms unseen feature inputs into predicted labels using pre-computed internal state.

3. Avoiding Data Leakage:
Evaluating a model on samples it trained on creates false confidence. Always guard your test split like production data.