Transformed targets changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
Transformed targets changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
The practical value of Transformed targets comes from understanding both the transformation and the boundary around it: what information is allowed to enter, what assumption is being made, and how you know the result is still valid after the transformation.
A beginner-friendly way to reason about it is to start with a tiny case where the correct result can be checked independently. Once the mechanism is clear, scale the exact same reasoning to larger tables, pipelines or models.
Fit transformation parameters on training data, apply exactly the learned transformation to validation/test/new data, and preserve the fitted transformer with the model.
The interactive view uses a concept-specific plot when the topic maps naturally to one; otherwise it uses a workflow view instead of leaving a broken placeholder.
Standardise continuous variables for a distance-based model while one-hot encoding nominal categories in the same pipeline.
A useful diagnostic question is: Could the same code still run successfully if the analytical assumption were wrong? If yes, add an explicit validation check rather than relying on execution success.
Keep the example small enough that you can inspect each stage manually.
# Purpose: demonstrate Transformed targets with a small, inspectable example.
# Follow the comments and printed stages to connect each operation with its result.
# Import the library or helper used in this example.
# Step 1 — Import the module so its functions/classes are available to the rest of this example.
import numpy as np
# Import the library or helper used in this example.
# Step 2 — Import the module so its functions/classes are available to the rest of this example.
import pandas as pd
# Import the library or helper used in this example.
# Step 3 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.compose import ColumnTransformer
# Import the library or helper used in this example.
# Step 4 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.impute import SimpleImputer
# Import the library or helper used in this example.
# Step 5 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.pipeline import Pipeline
# Import the library or helper used in this example.
# Step 6 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.preprocessing import OneHotEncoder, StandardScaler
# Import the library or helper used in this example.
# Step 7 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.linear_model import LogisticRegression
# Create a small labelled dataset that is easy to inspect by eye.
# Step 8 — Construct `X` as a tabular object with named columns for inspectable analysis.
X = pd.DataFrame({"age":[22,25,28,31,35,39,42,46,50,54,58,61],"income":[40,45,np.nan,50,55,60,62,68,72,76,80,85],"city":["A","A","B","B","A","C","C","A","B","C","A","B"]})
# Create the numerical values used in the calculation.
# Step 9 — Construct `y` as an array so vectorised numerical operations can be applied consistently.
y = np.array([0,0,0,0,0,1,0,1,1,1,1,1])
# Print this intermediate result so you can verify the workflow step by step.
# Step 10 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Data shape:", X.shape)
# Store this intermediate value with a descriptive name for the next step.
# Step 11 — Create `num` as the scaling object; its parameters will be learned from training data.
num = Pipeline([("impute",SimpleImputer(strategy="median")),("scale",StandardScaler())])
# Step 12 — Compute the right-hand expression and store its result in `cat` for the next step.
cat = OneHotEncoder(handle_unknown="ignore")
# Step 13 — Compute the right-hand expression and store its result in `pre` for the next step.
pre = ColumnTransformer([("num",num,["age","income"]),("cat",cat,["city"])])
# Configure the estimator or pipeline with the chosen settings.
# Step 14 — Instantiate `model` with the chosen algorithm/configuration before fitting it to data.
model = Pipeline([("pre",pre),("clf",LogisticRegression(max_iter=500))])
# Fit only on the training data so the model learns from allowed information.
# Step 15 — Fit the model or transformer, learning its parameters from the supplied training data.
model.fit(X,y)
# Print this intermediate result so you can verify the workflow step by step.
# Step 16 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 2 · Pipeline trained with steps:", list(model.named_steps))
# Print this intermediate result so you can verify the workflow step by step.
# Step 17 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · Training accuracy:", round(model.score(X,y),3))
# Generate class probabilities so confidence and thresholds can be inspected.
# Step 18 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · First probabilities:", model.predict_proba(X[:3])[:,1].round(3).tolist())STEP 1 · Data shape: (12, 3) STEP 2 · Pipeline trained with steps: ['pre', 'clf'] STEP 3 · Training accuracy: 0.833 STEP 3 · First probabilities: [0.042, 0.073, 0.227]