ColumnTransformer for mixed types in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct class for transforming columns.

ML Python

from sklearn.compose import [1]

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

AStandardScaler

BOneHotEncoder

CColumnTransformer

DSimpleImputer

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a transformer like StandardScaler instead of ColumnTransformer.

Confusing OneHotEncoder with ColumnTransformer.

The ColumnTransformer class is used to apply different transformations to different columns.

2fill in blank

medium

Complete the code to create a ColumnTransformer that applies OneHotEncoder to categorical columns.

ML Python

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder

ct = ColumnTransformer(transformers=[('cat', [1](), ['color', 'type'])], remainder='passthrough')

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

AOneHotEncoder

BStandardScaler

CSimpleImputer

DPCA

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler for categorical data.

Forgetting to import OneHotEncoder.

OneHotEncoder is used to convert categorical columns into one-hot numeric arrays.

3fill in blank

hard

Fix the error in the code to apply StandardScaler to numeric columns.

ML Python

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler

ct = ColumnTransformer(transformers=[('num', [1](), ['age', 'income'])], remainder='passthrough')

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

AOneHotEncoder

BStandardScaler

CSimpleImputer

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using OneHotEncoder for numeric columns.

Using LabelEncoder which is for labels, not features.

StandardScaler scales numeric features to have mean 0 and variance 1.

4fill in blank

hard

Fill both blanks to create a ColumnTransformer that imputes missing values in numeric columns and encodes categorical columns.

ML Python

from sklearn.compose import ColumnTransformer
from sklearn.impute import [1]
from sklearn.preprocessing import [2]

ct = ColumnTransformer(transformers=[('num', [1](), ['age', 'income']), ('cat', [2](), ['gender', 'city'])], remainder='passthrough')

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

ASimpleImputer

BOneHotEncoder

CStandardScaler

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler instead of SimpleImputer for missing values.

Using LabelEncoder for categorical features in ColumnTransformer.

SimpleImputer fills missing values, and OneHotEncoder encodes categorical features.

5fill in blank

hard

Fill all three blanks to create a pipeline that preprocesses numeric and categorical data, then fits a logistic regression model.

ML Python

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import [1]
from sklearn.impute import [2]
from sklearn.preprocessing import [3], StandardScaler

numeric_transformer = Pipeline(steps=[('imputer', [2]()), ('scaler', StandardScaler())])
categorical_transformer = Pipeline(steps=[('encoder', [3](handle_unknown='ignore'))])

ct = ColumnTransformer(transformers=[('num', numeric_transformer, ['age', 'income']), ('cat', categorical_transformer, ['gender', 'city'])])

model = Pipeline(steps=[('preprocessor', ct), ('classifier', [1]())])

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

ALogisticRegression

BSimpleImputer

COneHotEncoder

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using RandomForestClassifier instead of LogisticRegression as the model here.

Forgetting to use SimpleImputer in the numeric pipeline.

Not setting handle_unknown='ignore' in OneHotEncoder.

This pipeline imputes missing numeric values with SimpleImputer, encodes categorical data with OneHotEncoder, and fits a LogisticRegression model.

Practice

1. What is the main purpose of using ColumnTransformer in machine learning?

easy

A. To train multiple models on the same dataset

B. To apply different preprocessing steps to different columns in a dataset

C. To visualize data distributions

D. To split data into training and testing sets

Solution

1. Step 1: Understand the role of ColumnTransformer

   ColumnTransformer allows applying different transformations to different columns, such as scaling numbers and encoding text.

2. Step 2: Compare with other options

   Training models, visualizing data, or splitting data are different tasks not handled by ColumnTransformer.

3. Final Answer:

   To apply different preprocessing steps to different columns in a dataset -> Option B

4. Quick Check:

   ColumnTransformer = Different preprocessing per column [OK]

Hint: Think: Different columns, different treatments [OK]

Common Mistakes:

• Confusing ColumnTransformer with model training
• Thinking it splits data instead of transforming
• Assuming it visualizes data

2. Which of the following is the correct way to import ColumnTransformer from scikit-learn?

easy

A. from sklearn.feature_extraction import ColumnTransformer

B. from sklearn.preprocessing import ColumnTransformer

C. from sklearn.pipeline import ColumnTransformer

D. from sklearn.compose import ColumnTransformer

Solution

1. Step 1: Recall the module for ColumnTransformer

   ColumnTransformer is part of the compose module in scikit-learn.

2. Step 2: Verify other options

   Preprocessing, pipeline, and feature_extraction modules do not contain ColumnTransformer.

3. Final Answer:

   from sklearn.compose import ColumnTransformer -> Option D

4. Quick Check:

   ColumnTransformer is in compose module [OK]

Hint: Remember: compose module for combining transformers [OK]

Common Mistakes:

• Importing from preprocessing instead of compose
• Confusing pipeline with compose
• Trying to import from feature_extraction

3. Given the code below, what will be the output of print(transformed_data)?

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
import numpy as np

X = np.array([[1, 'red'], [2, 'blue'], [3, 'green']])

ct = ColumnTransformer([
    ('num', StandardScaler(), [0]),
    ('cat', OneHotEncoder(), [1])
])

transformed_data = ct.fit_transform(X)
print(transformed_data)

medium

A. A numpy array with scaled numbers and one-hot encoded colors

B. A list of original values without changes

C. An error because StandardScaler cannot handle strings

D. A numpy array with only scaled numbers

Solution

1. Step 1: Understand ColumnTransformer setup

   Column 0 (numbers) is scaled; column 1 (colors) is one-hot encoded.

2. Step 2: Predict output structure

   Output is a numpy array combining scaled numeric values and one-hot encoded categorical values.

3. Final Answer:

   A numpy array with scaled numbers and one-hot encoded colors -> Option A

4. Quick Check:

   Mixed types transformed correctly = scaled + one-hot [OK]

Hint: Remember: ColumnTransformer applies each transformer to specified columns [OK]

Common Mistakes:

• Expecting original data without transformation
• Thinking StandardScaler will fail on mixed data
• Ignoring one-hot encoding effect

4. What is wrong with the following code snippet using ColumnTransformer?

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
import numpy as np

X = np.array([[1, 'red'], [2, 'blue'], [3, 'green']])

ct = ColumnTransformer([
    ('num', StandardScaler(), [0, 1]),
    ('cat', OneHotEncoder(), [1])
])

transformed_data = ct.fit_transform(X)

medium

A. StandardScaler is applied to a string column causing an error

B. OneHotEncoder is applied to a numeric column causing an error

C. ColumnTransformer requires numeric data only

D. No error, code runs fine

Solution

1. Step 1: Check columns assigned to StandardScaler

   StandardScaler is applied to columns 0 and 1, but column 1 contains strings.

2. Step 2: Understand why this causes an error

   StandardScaler cannot process string data, so this will raise a type error.

3. Final Answer:

   StandardScaler is applied to a string column causing an error -> Option A

4. Quick Check:

   Scaler on strings = error [OK]

Hint: Scaler only works on numeric columns [OK]

Common Mistakes:

• Applying scaler to categorical columns
• Assuming ColumnTransformer auto-detects types
• Ignoring column indices in transformer

5. You have a dataset with numeric columns ['age', 'income'] and categorical columns ['city', 'gender']. You want to scale numeric columns and one-hot encode categorical columns using ColumnTransformer. Which code snippet correctly sets this up?

hard

A. ColumnTransformer([('num', OneHotEncoder(), ['age', 'income']), ('cat', StandardScaler(), ['city', 'gender'])])

B. ColumnTransformer([('num', StandardScaler(), ['city', 'gender']), ('cat', OneHotEncoder(), ['age', 'income'])])

C. ColumnTransformer([('num', StandardScaler(), ['age', 'income']), ('cat', OneHotEncoder(), ['city', 'gender'])])

D. ColumnTransformer([('num', StandardScaler(), ['age']), ('cat', OneHotEncoder(), ['income', 'city', 'gender'])])

Solution

1. Step 1: Identify correct transformers for each column type

   Numeric columns should be scaled with StandardScaler; categorical columns should be one-hot encoded.

2. Step 2: Match columns to transformers correctly

   ColumnTransformer([('num', StandardScaler(), ['age', 'income']), ('cat', OneHotEncoder(), ['city', 'gender'])]) assigns numeric columns to StandardScaler and categorical columns to OneHotEncoder correctly.

3. Final Answer:

   ColumnTransformer([('num', StandardScaler(), ['age', 'income']), ('cat', OneHotEncoder(), ['city', 'gender'])]) -> Option C

4. Quick Check:

   Numeric scaled + categorical one-hot = ColumnTransformer([('num', StandardScaler(), ['age', 'income']), ('cat', OneHotEncoder(), ['city', 'gender'])]) [OK]

Hint: Match scaler to numbers, encoder to categories [OK]

Common Mistakes:

• Swapping transformers between numeric and categorical
• Mixing columns in wrong transformer
• Leaving out columns