Linear and Logistic Regression Implementation Questions

Covers the fundamentals and implementation details of linear regression for continuous prediction and logistic regression for binary or multiclass classification. Candidates should understand model formulation, hypothesis functions, and the intuition behind fitting a line or hyperplane for regression and using a sigmoid or softmax function for classification. Include loss functions such as mean squared error for regression and cross entropy loss for classification, optimization methods including gradient descent and variants, regularization techniques, feature engineering and scaling, metrics for evaluation such as mean absolute error and accuracy and area under curve, and hyperparameter selection and validation strategies. Expect discussion of practical implementation using numerical libraries and machine learning toolkits, trade offs and limitations of each approach, numerical stability, and common pitfalls such as underfitting and overfitting.

EasyTechnical

0 practiced

Implement Ordinary Least Squares linear regression in Python using NumPy. Write a function fit_ols(X, y) that returns coefficients w and intercept b using the normal equation and handles non-invertible X^T X via pseudoinverse. Assume X is shape (n_samples, n_features). Show example usage and mention numerical stability considerations.

MediumTechnical

0 practiced

Compare grid search, random search, and Bayesian optimization for hyperparameter tuning of linear/logistic models. Discuss computational budgets, dimensionality effects, exploration-exploitation trade-offs, and how early-stopping strategies (successive halving) reduce compute.

HardTechnical

0 practiced

Explain the Karush-Kuhn-Tucker (KKT) optimality conditions for L1-regularized logistic regression. Describe the subgradient condition at zero coefficients, how these conditions guide coordinate-descent and active-set algorithms, and what checks you would perform to verify optimality of a fitted sparse model.

HardSystem Design

0 practiced

Design a CI/CD pipeline for training, validating, packaging, and deploying linear and logistic regression models. Include unit tests for preprocessing and gradient computations, model artifact versioning, automated performance gates, staging and canary deployments, rollback procedures, and monitoring hooks for production quality assurance.

MediumTechnical

0 practiced

Implement batch gradient descent for linear regression in Python using NumPy. Write gd_linear(X, y, lr, n_iters, tol) that returns weights; use vectorized operations, compute MSE as objective, and include stopping criteria based on change in loss. Discuss the role of learning rate and feature scaling for convergence.

Unlock Full Question Bank

Get access to hundreds of Linear and Logistic Regression Implementation interview questions and detailed answers.

Join thousands of developers preparing for their dream job.