Features, targets and labels 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.
Features, targets and labels 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 Features, targets and labels 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 Features, targets and labels 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 pandas as pd
# Create a small labelled dataset that is easy to inspect by eye.
# Step 2 — Construct `df` as a tabular object with named columns for inspectable analysis.
df=pd.DataFrame({"age":[22,25,28,31,34,37,40,43,46,49,52,55],"city":["A","A","B","B","A","C","C","A","B","C","A","B"],"sales":[120,135,128,160,170,166,180,195,210,205,225,240]})
# Print this intermediate result so you can verify the workflow step by step.
# Step 3 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Shape:",df.shape)
# Print this intermediate result so you can verify the workflow step by step.
# Step 4 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Dtypes:",df.dtypes.astype(str).to_dict())
# Print this intermediate result so you can verify the workflow step by step.
# Step 5 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 2 · Numeric summary:\n",df[["age","sales"]].describe().round(2).to_string())
# Print this intermediate result so you can verify the workflow step by step.
# Step 6 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · Sales by city:\n",df.groupby("city")["sales"].agg(["count","mean"]).round(2).to_string())STEP 1 · Shape: (12, 3)
STEP 1 · Dtypes: {'age': 'int64', 'city': 'object', 'sales': 'int64'}
STEP 2 · Numeric summary:
age sales
count 12.00 12.00
mean 38.50 177.83
std 10.82 38.55
min 22.00 120.00
25% 30.25 153.75
50% 38.50 175.00
75% 46.75 206.25
max 55.00 240.00
STEP 3 · Sales by city:
count mean
city
A 5 169.00
B 4 184.50
C 3 183.67