Polynomial regression pipeline in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the class for polynomial features.

ML Python

from sklearn.preprocessing import [1]

Drag options to blanks, or click blank then click option'

AStandardScaler

BLinearRegression

CPolynomialFeatures

Dtrain_test_split

Attempts:

3 left

💡 Hint

Common Mistakes

Importing LinearRegression instead of PolynomialFeatures

Importing train_test_split which is for splitting data

Importing StandardScaler which is for scaling features

The PolynomialFeatures class is used to generate polynomial and interaction features from the original features.

2fill in blank

medium

Complete the code to create a polynomial features transformer of degree 3.

ML Python

poly = PolynomialFeatures(degree=[1])

Drag options to blanks, or click blank then click option'

A3

B2

C1

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Using degree 1 which does not add polynomial features

Using degree 2 which adds only quadratic terms

Using degree 4 which is higher than requested

Setting degree=3 creates polynomial features up to cubic terms.

3fill in blank

hard

Fix the error in the code to fit the polynomial regression model.

ML Python

model = LinearRegression()
X_poly = poly.[1](X)
model.fit(X_poly, y)

Drag options to blanks, or click blank then click option'

Afit

Btransform

Cfit_transform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit_transform instead of transform causing double fitting

Using fit which returns the transformer itself, not transformed data

Using predict which is for making predictions

We first fit the poly transformer separately, then use transform to convert X to polynomial features before fitting the model.

4fill in blank

hard

Fill both blanks to create a pipeline that first transforms features then fits a linear regression.

ML Python

from sklearn.pipeline import Pipeline
pipeline = Pipeline([
    ('poly', [1](degree=2)),
    ('linear', [2]())
])

Drag options to blanks, or click blank then click option'

APolynomialFeatures

BLinearRegression

CStandardScaler

DLogisticRegression

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler instead of PolynomialFeatures for the first step

Using LogisticRegression which is for classification

Swapping the order of steps

The pipeline first applies PolynomialFeatures to create polynomial terms, then fits a LinearRegression model.

5fill in blank

hard

Fill all three blanks to train the pipeline and predict on test data.

ML Python

pipeline.fit([1], [2])
predictions = pipeline.[3](X_test)

Drag options to blanks, or click blank then click option'

AX_train

By_train

Cpredict

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using y_train as first argument to fit

Using fit instead of predict for predictions

Using X_test in fit instead of training data

We fit the pipeline on training features and targets, then use predict to get predictions on test features.

Practice

1.

What is the main purpose of using polynomial regression instead of simple linear regression?

easy

A. To fit curved relationships between variables

B. To reduce the number of features

C. To speed up training time

D. To handle missing data automatically

Solution

1. Step 1: Understand linear regression limitation

   Linear regression fits straight lines, which cannot capture curves in data.

2. Step 2: Role of polynomial regression

   Polynomial regression fits curved lines by adding powers of features, capturing non-linear patterns.

3. Final Answer:

   To fit curved relationships between variables -> Option A

4. Quick Check:

   Polynomial regression = curved fit [OK]

Hint: Polynomial regression fits curves, not just straight lines [OK]

Common Mistakes:

• Thinking polynomial regression reduces features
• Assuming it speeds up training
• Believing it handles missing data automatically

2.

Which of the following is the correct way to create a polynomial regression pipeline in Python using sklearn?

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

pipeline = Pipeline([
    ('poly', PolynomialFeatures(degree=2)),
    ('linear', LinearRegression())
])

easy

A. pipeline = Pipeline([('poly', PolynomialFeatures(degree=2)), ('linear', LinearRegression())])

B. pipeline = Pipeline([('linear', LinearRegression()), ('poly', PolynomialFeatures(degree=2))])

C. pipeline = Pipeline([('poly', LinearRegression()), ('linear', PolynomialFeatures(degree=2))])

D. pipeline = Pipeline([('poly', PolynomialFeatures()), ('linear', LinearRegression(degree=2))])

Solution

1. Step 1: Order of pipeline steps

   PolynomialFeatures must come before LinearRegression to transform data first.

2. Step 2: Correct usage of classes and parameters

   PolynomialFeatures takes degree parameter; LinearRegression does not take degree.

3. Final Answer:

   pipeline = Pipeline([('poly', PolynomialFeatures(degree=2)), ('linear', LinearRegression())]) -> Option A

4. Quick Check:

   PolynomialFeatures before LinearRegression [OK]

Hint: Put PolynomialFeatures before LinearRegression in pipeline [OK]

Common Mistakes:

• Swapping order of pipeline steps
• Passing degree to LinearRegression
• Omitting degree in PolynomialFeatures

3.

Given the following code, what will print(y_pred) output?

import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

X = np.array([[1], [2], [3]])
y = np.array([1, 4, 9])

pipeline = Pipeline([
    ('poly', PolynomialFeatures(degree=2)),
    ('linear', LinearRegression())
])
pipeline.fit(X, y)
y_pred = pipeline.predict(np.array([[4]]))
print(np.round(y_pred, 2))

medium

A. [10.0]

B. [8.0]

C. [4.0]

D. [16.0]

Solution

1. Step 1: Understand data and model

   X = [[1],[2],[3]] with y = [1,4,9] fits y = x^2 perfectly.

2. Step 2: Predict for X=4 using polynomial degree 2

   Model learns y = x^2, so prediction at 4 is 4^2 = 16.

3. Final Answer:

   [16.0] -> Option D

4. Quick Check:

   4 squared = 16 [OK]

Hint: Polynomial degree 2 fits squares; predict 4^2 = 16 [OK]

Common Mistakes:

• Ignoring polynomial transformation
• Predicting linear value instead of squared
• Rounding errors without np.round

4.

Identify the error in this polynomial regression pipeline code:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

pipeline = Pipeline([
    ('linear', LinearRegression()),
    ('poly', PolynomialFeatures(degree=3))
])

pipeline.fit(X_train, y_train)

medium

A. LinearRegression should not be used in pipeline

B. The order of pipeline steps is incorrect

C. PolynomialFeatures degree must be 2, not 3

D. Missing import for X_train and y_train

Solution

1. Step 1: Check pipeline step order

   PolynomialFeatures must come before LinearRegression to transform data first.

2. Step 2: Confirm degree and imports

   Degree 3 is valid; imports for data are assumed outside snippet.

3. Final Answer:

   The order of pipeline steps is incorrect -> Option B

4. Quick Check:

   PolynomialFeatures before LinearRegression [OK]

Hint: PolynomialFeatures must be first in pipeline [OK]

Common Mistakes:

• Swapping order of steps
• Thinking degree must be 2
• Confusing missing data imports with pipeline error

5.

You want to model a dataset with a complex curve. You try polynomial regression with degree=2 but the fit is poor. What is the best next step?

hard

A. Remove polynomial features and use linear regression only

B. Decrease the polynomial degree to avoid overfitting

C. Increase the polynomial degree to capture more complexity

D. Use degree=2 but reduce training data size

Solution

1. Step 1: Understand model complexity and fit

   Degree 2 polynomial may be too simple for complex curves, causing poor fit.

2. Step 2: Adjust polynomial degree

   Increasing degree allows model to fit more complex patterns, improving fit quality.

3. Final Answer:

   Increase the polynomial degree to capture more complexity -> Option C

4. Quick Check:

   Higher degree = better complex fit [OK]

Hint: Raise degree to fit complex curves better [OK]

Common Mistakes:

• Lowering degree when fit is poor
• Removing polynomial features unnecessarily
• Reducing data size instead of model complexity