Machine Learning Engineer interview questions

Supervised learning involves training a model on labeled data, meaning the model learns from inputs that are paired with the correct output. Unsupervised learning, on the other hand, deals with finding patterns in data without any labels or predefined outputs.

Missing data can be handled in several ways, such as removing or imputing missing values. Imputation techniques include using the mean or median to fill in missing values, or more sophisticated methods like K-Nearest Neighbors or regression imputation.

The bias-variance tradeoff is the balance between the error due to bias, which is the error from excessively simplistic models, and variance, which is the error from overly complex models. The goal is to minimize both to reduce overall error.

Common techniques for feature selection include Recursive Feature Elimination, tree-based methods, and using model coefficients. Feature selection helps improve model performance by reducing overfitting and computational cost.

Model performance can be evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC for classification tasks, and mean squared error, mean absolute error, and R-squared for regression tasks.

Cross-validation is a technique where the dataset is split into multiple subsets, and the model is trained and tested multiple times on these sets. It's important because it provides a more reliable estimate of model performance by reducing overfitting and ensuring robustness.

Overfitting occurs when a model learns the training data too well, capturing noise and fluctuations rather than the underlying patterns. This leads to poor generalization to new data, resulting in high variance and poor test performance.

Ensemble learning is a machine learning technique where multiple models are combined to improve overall performance. Common ensemble methods include bagging, boosting, and stacking, each leveraging different types of model combinations.

Algorithm choice depends on the nature of the problem, dataset size, desired model complexity, interpretability, and computational resources. Trial and error, combined with domain knowledge and experience, guide the selection process.

Data preprocessing is crucial as it directly impacts model performance. It involves cleaning and transforming raw data into a suitable format, addressing issues like missing values, inconsistent formats, noise reduction, and feature scaling.