Feature scaling (StandardScaler, MinMaxScaler) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the StandardScaler from scikit-learn.

ML Python

from sklearn.preprocessing import [1]

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

ARobustScaler

BMinMaxScaler

CNormalizer

DStandardScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing MinMaxScaler instead of StandardScaler

Trying to import from sklearn.preprocessing.scale instead

The StandardScaler is imported from sklearn.preprocessing to scale features by removing the mean and scaling to unit variance.

2fill in blank

medium

Complete the code to create a MinMaxScaler instance.

ML Python

scaler = [1]()

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

AMinMaxScaler

BStandardScaler

CNormalizer

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler when MinMaxScaler is needed

Trying to instantiate LabelEncoder which is for labels, not features

The MinMaxScaler scales features to a given range, usually between 0 and 1.

3fill in blank

hard

Fix the error in the code to fit the scaler to the data array X.

ML Python

scaler.[1](X)

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

Atransform

Bfit_transform

Cfit

Dscale

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform before fitting the scaler

Using fit_transform when only fitting is needed

The fit method computes the mean and std (for StandardScaler) or min and max (for MinMaxScaler) from the data.

4fill in blank

hard

Fill both blanks to scale the data X using fit and transform methods.

ML Python

scaler.[1](X)
X_scaled = scaler.[2](X)

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

Afit

Btransform

Cfit_transform

Dinverse_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Calling transform without fitting first

Using fit_transform instead of separate fit and transform

To scale using separate steps: first fit the scaler on data to learn parameters, then transform the data.

5fill in blank

hard

Fill all three blanks to create a StandardScaler, fit it on training data, and transform test data.

ML Python

scaler = [1]()
scaler.[2](X_train)
X_test_scaled = scaler.[3](X_test)

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

AMinMaxScaler

Bfit

Ctransform

DStandardScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Fitting scaler on test data instead of training data

Using MinMaxScaler instead of StandardScaler here

First, create a StandardScaler instance. Then fit it on training data to learn scaling parameters. Finally, transform the test data using those parameters.

Practice

1. What is the main purpose of using StandardScaler in feature scaling?

easy

A. To center data to mean 0 and scale to unit variance

B. To rescale data to a fixed range like 0 to 1

C. To remove missing values from the dataset

D. To convert categorical data into numbers

Solution

1. Step 1: Understand what StandardScaler does

   StandardScaler subtracts the mean and divides by the standard deviation for each feature.

2. Step 2: Compare with other options

   MinMaxScaler rescales to a fixed range, not StandardScaler. Removing missing values or encoding categories is unrelated.

3. Final Answer:

   To center data to mean 0 and scale to unit variance -> Option A

4. Quick Check:

   StandardScaler = mean 0, variance 1 [OK]

Hint: StandardScaler centers and scales data, MinMaxScaler rescales range [OK]

Common Mistakes:

• Confusing StandardScaler with MinMaxScaler
• Thinking StandardScaler changes data range to 0-1
• Mixing feature scaling with data cleaning

2. Which of the following is the correct way to import and create a MinMaxScaler object in Python using scikit-learn?

easy

A. from sklearn.preprocessing import StandardScaler
scaler = MinMaxScaler()

B. import sklearn.MinMaxScaler
scaler = MinMaxScaler()

C. from sklearn.scale import MinMaxScaler
scaler = MinMaxScaler()

D. from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()

Solution

1. Step 1: Recall correct import path

   MinMaxScaler is in sklearn.preprocessing module, so import from there.

2. Step 2: Check object creation syntax

   Creating scaler with MinMaxScaler() is correct. Other options have wrong module or mismatch.

3. Final Answer:

   from sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler() -> Option D

4. Quick Check:

   Correct import and instantiation [OK]

Hint: MinMaxScaler is in sklearn.preprocessing, not sklearn.scale [OK]

Common Mistakes:

• Using wrong module name like sklearn.scale
• Importing StandardScaler but creating MinMaxScaler
• Trying to import MinMaxScaler directly from sklearn

3. Given the data array [[2], [4], [6], [8]], what will be the output after applying MinMaxScaler()?

medium

A. [[0.0], [0.33], [0.67], [1.0]]

B. [[0.0], [0.5], [1.0], [1.5]]

C. [[-1.34], [-0.45], [0.45], [1.34]]

D. [[2], [4], [6], [8]]

Solution

1. Step 1: Understand MinMaxScaler formula

   MinMaxScaler scales each value x as (x - min) / (max - min). Here min=2, max=8.

2. Step 2: Calculate scaled values

   For 2: (2-2)/(8-2)=0.0; 4: (4-2)/6=0.33; 6: (6-2)/6=0.67; 8: (8-2)/6=1.0.

3. Final Answer:

   [[0.0], [0.33], [0.67], [1.0]] -> Option A

4. Quick Check:

   MinMaxScaler scales between 0 and 1 [OK]

Hint: MinMaxScaler maps min to 0 and max to 1 linearly [OK]

Common Mistakes:

• Confusing MinMaxScaler with StandardScaler output
• Not calculating min and max correctly
• Expecting scaled values outside 0-1 range

4. You applied StandardScaler on a dataset but got an error: ValueError: Input contains NaN. What is the best way to fix this?

medium

A. Use StandardScaler without fitting

B. Remove or fill missing values before scaling

C. Ignore the error and continue

D. Change StandardScaler to MinMaxScaler

Solution

1. Step 1: Understand cause of error

   StandardScaler cannot handle NaN (missing) values directly, causing ValueError.

2. Step 2: Fix by preprocessing data

   Remove rows with NaN or fill them with mean/median before scaling to avoid error.

3. Final Answer:

   Remove or fill missing values before scaling -> Option B

4. Quick Check:

   Handle NaN before scaling [OK]

Hint: Clean missing data before scaling to avoid errors [OK]

Common Mistakes:

• Switching scaler without fixing NaN
• Ignoring error and expecting correct results
• Trying to scale without fitting the scaler

5. You have two features: height (in cm) ranging from 150 to 190, and income (in thousands) ranging from 20 to 200. You want to use a model sensitive to feature scale. Which scaler should you use and why?

hard

A. Use StandardScaler to center and scale both features to mean 0 and variance 1

B. Use no scaling because features have different units

C. Use MinMaxScaler to rescale both features to 0-1 range

D. Use MinMaxScaler only on income, leave height as is

Solution

1. Step 1: Understand feature ranges and model sensitivity

   Height and income have different units and ranges; model sensitive to scale needs comparable ranges.

2. Step 2: Choose scaler based on range preservation

   MinMaxScaler rescales both features to 0-1, preserving relative differences and making scales comparable.

3. Step 3: Why not StandardScaler?

   StandardScaler centers data but may produce negative values and does not bound range, which can confuse some models.

4. Final Answer:

   Use MinMaxScaler to rescale both features to 0-1 range -> Option C

5. Quick Check:

   MinMaxScaler = rescale to fixed range [OK]

Hint: MinMaxScaler makes features comparable by fixed range [OK]

Common Mistakes:

• Using StandardScaler without considering model needs
• Not scaling features with different units
• Scaling only one feature and not the other