Scikit-Learn Interview Questions

This document provides a curated list of Scikit-Learn interview questions commonly asked in technical interviews for Machine Learning Engineer, Data Scientist, and AI/ML roles. It covers fundamental concepts to advanced machine learning techniques, model evaluation, and production deployment.

This is updated frequently but right now this is the most exhaustive list of type of questions being asked.

Code Examples

1. Building a Custom Transformer

from sklearn.base import BaseEstimator, TransformerMixin

class OutlierRemover(BaseEstimator, TransformerMixin):
    def __init__(self, factor=1.5):
        self.factor = factor

    def fit(self, X, y=None):
        self.Q1 = X.quantile(0.25)
        self.Q3 = X.quantile(0.75)
        self.IQR = self.Q3 - self.Q1
        return self

    def transform(self, X):
        return X[~((X < (self.Q1 - self.factor * self.IQR)) | 
                   (X > (self.Q3 + self.factor * self.IQR))).any(axis=1)]

2. Nested Cross-Validation

from sklearn.model_selection import GridSearchCV, cross_val_score, KFold
from sklearn.svm import SVC
import numpy as np

# Inner loop for hyperparameter tuning
p_grid = {"C": [1, 10, 100], "gamma": [0.01, 0.1]}
svm = SVC(kernel="rbf")
inner_cv = KFold(n_splits=4, shuffle=True, random_state=1)
clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv)

# Outer loop for model evaluation
outer_cv = KFold(n_splits=4, shuffle=True, random_state=1)
nested_score = cross_val_score(clf, X_iris, y_iris, cv=outer_cv)

print(f"Nested CV Score: {nested_score.mean():.3f} +/- {nested_score.std():.3f}")

3. Pipeline with ColumnTransformer

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)])

clf = Pipeline(steps=[('preprocessor', preprocessor),
                      ('classifier', LogisticRegression())])

Questions asked in Google interview

• How would you implement a custom loss function in Scikit-Learn?
• Explain how to handle data leakage in cross-validation
• Write code to implement nested cross-validation with hyperparameter tuning
• How would you optimize a model for minimal inference latency?
• Explain the bias-variance tradeoff with specific examples
• How would you implement model calibration for probability estimates?
• Write code to implement stratified sampling for imbalanced multi-class
• How would you handle concept drift in production ML systems?
• Explain how to implement feature importance with SHAP values
• How would you optimize memory for large sparse datasets?

Questions asked in Amazon interview

• Write code to implement a complete ML pipeline for customer churn
• How would you handle high-cardinality categorical features?
• Explain the difference between different cross-validation strategies
• Write code to implement time series cross-validation
• How would you implement model monitoring in production?
• Explain how to handle missing data in production systems
• Write code to implement custom scoring functions
• How would you implement A/B testing for model comparison?
• Explain how to optimize hyperparameters efficiently
• How would you handle data leakage in feature engineering?

Questions asked in Meta interview

• Write code to implement user engagement prediction pipeline
• How would you implement multi-label classification for content tagging?
• Explain how to handle extremely imbalanced datasets
• Write code to implement custom transformers for text features
• How would you implement feature selection for high-dimensional data?
• Explain how to implement model interpretability
• Write code to implement online learning with partial_fit
• How would you implement model calibration?
• Explain how to prevent overfitting in ensemble models
• How would you implement multivariate predictions?

Questions asked in Microsoft interview

• Explain the Scikit-Learn estimator API design principles
• Write code to implement custom estimators extending BaseEstimator
• How would you implement regularization selection?
• Explain the differences between solver options in LogisticRegression
• Write code to implement feature engineering pipelines
• How would you optimize model training time?
• Explain how to implement model persistence correctly
• Write code to implement cross-validation with custom folds
• How would you handle numerical stability issues?
• Explain how to implement reproducible ML experiments

Questions asked in Netflix interview

• Write code to implement recommendation feature engineering
• How would you implement content classification at scale?
• Explain how to handle user behavior data for ML
• Write code to implement streaming quality prediction
• How would you implement real-time inference optimization?
• Explain how to implement model monitoring and retraining
• Write code to implement cohort-based model evaluation
• How would you handle seasonality in user data?
• Explain how to implement A/B testing for ML models
• How would you implement customer lifetime value prediction?

Questions asked in Apple interview

• Write code to implement privacy-preserving ML pipelines
• How would you implement on-device ML model optimization?
• Explain how to handle sensor data for ML
• Write code to implement quality control classification
• How would you implement model quantization for deployment?
• Explain best practices for production ML systems
• Write code to implement automated model retraining
• How would you handle data versioning?
• Explain how to implement cross-platform model deployment
• How would you implement model security?

Additional Resources

• Official Scikit-Learn Documentation
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Python Data Science Handbook
• Introduction to Machine Learning with Python
• Scikit-Learn Course by Andreas Mueller