Feature engineering pipelines in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple feature pipeline step using scikit-learn.

MLOps

from sklearn.preprocessing import [1]

scaler = [1]()

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

AStandardScaler

BMinMaxScaler

CPCA

DOneHotEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using PCA here instead of a scaler.

Choosing OneHotEncoder which is for categorical data, not scaling.

The StandardScaler is a common feature scaling step in pipelines to standardize features.

2fill in blank

medium

Complete the code to combine two feature transformers into a pipeline.

MLOps

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

pipeline = Pipeline(steps=[('scaler', StandardScaler()), ('[1]', PCA(n_components=2))])

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

Apca

Btransformer

Creducer

Dencoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using generic names like 'transformer' which are less descriptive.

Using 'encoder' which is unrelated to PCA.

The name 'pca' is a common step name for the PCA transformer in pipelines.

3fill in blank

hard

Fix the error in the pipeline code by completing the missing method call.

MLOps

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

pipeline = Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA(n_components=2))])
X_transformed = pipeline.[1](X_train)

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

Atransform

Bfit

Cfit_transform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' alone returns the pipeline itself, not transformed data.

Using 'predict' is for models, not transformers.

The fit_transform method fits the pipeline to the data and then transforms it in one step.

4fill in blank

hard

Fill both blanks to create a ColumnTransformer that applies scaling to numeric and encoding to categorical features.

MLOps

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

preprocessor = ColumnTransformer(transformers=[('num', [1], numeric_features), ('cat', [2], categorical_features)])

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

AStandardScaler

BOneHotEncoder

CPCA

DMinMaxScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Using PCA for categorical data.

Mixing up scaler and encoder roles.

Numeric features are scaled with StandardScaler, and categorical features are encoded with OneHotEncoder.

5fill in blank

hard

Fill all three blanks to build a pipeline that preprocesses data and fits a logistic regression model.

MLOps

from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

pipeline = Pipeline(steps=[('preprocessor', [1]), ('classifier', LogisticRegression([2]=[3]))])

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

Apreprocessor

Bmax_iter

C100

DStandardScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Passing scaler directly instead of the preprocessor pipeline.

Using wrong parameter names for LogisticRegression.

The pipeline uses a preprocessor step, and LogisticRegression is set with max_iter=100 to control iterations.

Practice

1. What is the main purpose of a feature engineering pipeline in MLOps?

easy

A. To automate and standardize data preparation steps

B. To deploy machine learning models to production

C. To monitor model performance after deployment

D. To collect raw data from external sources

Solution

1. Step 1: Understand the role of feature engineering pipelines

   Feature engineering pipelines automate the process of transforming raw data into features for model training and testing.

2. Step 2: Differentiate from other MLOps tasks

   Deploying models, monitoring, and data collection are separate tasks from feature engineering pipelines.

3. Final Answer:

   To automate and standardize data preparation steps -> Option A

4. Quick Check:

   Feature engineering pipeline = automate data prep [OK]

Hint: Feature pipelines automate data prep, not deployment or monitoring [OK]

Common Mistakes:

• Confusing feature pipelines with model deployment
• Thinking pipelines collect raw data
• Mixing up monitoring with feature engineering

2. Which of the following is the correct way to define a simple feature engineering pipeline step using scikit-learn's Pipeline?

easy

A. pipeline = Pipeline([('scaler', StandardScaler()), ('pca', PCA(n_components=2))])

B. pipeline = Pipeline('scaler', StandardScaler(), 'pca', PCA(n_components=2))

C. pipeline = Pipeline({'scaler': StandardScaler(), 'pca': PCA(n_components=2)})

D. pipeline = Pipeline(StandardScaler(), PCA(n_components=2))

Solution

1. Step 1: Recall scikit-learn Pipeline syntax

   Pipeline expects a list of tuples, each tuple with a name and a transformer object.

2. Step 2: Check each option's syntax

   pipeline = Pipeline([('scaler', StandardScaler()), ('pca', PCA(n_components=2))]) correctly uses a list of tuples. Options B, C, and D use incorrect argument formats.

3. Final Answer:

   pipeline = Pipeline([('scaler', StandardScaler()), ('pca', PCA(n_components=2))]) -> Option A

4. Quick Check:

   Pipeline needs list of (name, transformer) tuples [OK]

Hint: Pipeline needs list of (name, transformer) tuples [OK]

Common Mistakes:

• Passing arguments without list brackets
• Using dict instead of list of tuples
• Omitting step names in pipeline

3. Given the following pipeline code, what will be the output of pipeline.transform([[0, 0], [1, 1]])?

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

pipeline = Pipeline([
  ('scaler', StandardScaler()),
  ('pca', PCA(n_components=1))
])
pipeline.fit([[0, 0], [1, 1]])
result = pipeline.transform([[0, 0], [1, 1]])
print(result)

medium

A. Error: PCA requires more than one sample

B. [[0. 0.]
[1. 1.]]

C. [[0.5]
[0.5]]

D. [[-1.41421356]
[ 1.41421356]]

Solution

1. Step 1: Understand pipeline steps

   First, data is scaled to zero mean and unit variance, then PCA reduces to 1 component.

2. Step 2: Calculate transformed output

   Scaling [[0,0],[1,1]] centers data, PCA finds principal component; output is approximately [[-1.41421356],[1.41421356]].

3. Final Answer:

   [[-1.41421356] [ 1.41421356]] -> Option D

4. Quick Check:

   Scaling + PCA output = [[-1.41421356] [ 1.41421356]] [OK]

Hint: Scaling centers data; PCA output is principal component values [OK]

Common Mistakes:

• Expecting original data as output
• Confusing PCA output shape
• Assuming error due to small data

4. You have this pipeline code but it raises an error: ValueError: Expected 2D array, got 1D array instead. What is the likely cause?

pipeline = Pipeline([
  ('scaler', StandardScaler()),
  ('pca', PCA(n_components=1))
])
pipeline.fit([1, 2, 3, 4])

medium

A. Pipeline steps must be functions, not classes

B. Input to fit should be 2D array, not 1D list

C. StandardScaler requires integer inputs only

D. PCA cannot have n_components=1

Solution

1. Step 1: Analyze error message

   The error says input is 1D but 2D is expected for fit method.

2. Step 2: Check input format

   Input [1, 2, 3, 4] is a 1D list; fit expects 2D array like [[1], [2], [3], [4]].

3. Final Answer:

   Input to fit should be 2D array, not 1D list -> Option B

4. Quick Check:

   fit input shape must be 2D [OK]

Hint: fit() needs 2D array shape, not flat list [OK]

Common Mistakes:

• Passing 1D list instead of 2D array
• Misunderstanding PCA parameter limits
• Thinking StandardScaler restricts input types

5. You want to create a feature engineering pipeline that handles missing values by filling them with the median, then scales features, and finally selects the top 3 features using a model-based selector. Which pipeline setup is correct?

hard

A. Pipeline([('scaler', StandardScaler()), ('imputer', SimpleImputer(strategy='median')), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))])

B. Pipeline([('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3)), ('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])

C. Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))])

D. Pipeline([('imputer', SimpleImputer(strategy='mean')), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3)), ('scaler', StandardScaler())])

Solution

1. Step 1: Order pipeline steps logically

   Missing values must be handled first, then scaling, then feature selection.

2. Step 2: Check each option's correctness

   Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))]) follows correct order and uses median for imputation. Pipeline([('scaler', StandardScaler()), ('imputer', SimpleImputer(strategy='median')), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))]) swaps imputer and scaler incorrectly. Pipeline([('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3)), ('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) starts with selector which needs complete data. Pipeline([('imputer', SimpleImputer(strategy='mean')), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3)), ('scaler', StandardScaler())]) uses mean instead of median and wrong order.

3. Final Answer:

   Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('selector', SelectFromModel(estimator=RandomForestClassifier(), max_features=3))]) -> Option C

4. Quick Check:

   Impute -> scale -> select features [OK]

Hint: Impute missing -> scale -> select features in pipeline order [OK]

Common Mistakes:

• Placing scaler before imputer
• Selecting features before imputing missing values
• Using mean instead of median when median is required