Knew this one but fumbled the articulation.
Start by defining bias and variance clearly, then explain the trade-off and its impact on model performance (underfitting vs. overfitting). Use a concrete example or a diagram to illustrate, and discuss strategies to balance the trade-off in practice.
Pro tip: Relate the trade-off to real-world engineering decisions, such as choosing model complexity or regularization, and mention how Meta's large-scale systems might require different trade-offs based on latency, accuracy, and resource constraints.
Clearly define bias as error from erroneous assumptions (underfitting) and variance as sensitivity to fluctuations in the training set (overfitting).
Describe how increasing model complexity decreases bias but increases variance, and vice versa, leading to a U-shaped test error curve.
Discuss how high bias leads to underfitting (poor on both train and test) and high variance leads to overfitting (good on train, poor on test).
Mention techniques like cross-validation, regularization (L1/L2), ensemble methods (bagging/boosting), and early stopping to manage the trade-off.
Connect to real-world examples, such as how Meta might prioritize low variance for stable predictions in production or accept higher bias for faster inference.
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L1 vs L2 is pretty standard but I got a little tangled explaining why L1 produces sparse weights.
Start by defining L1 and L2 regularization mathematically and their effects on model weights, then compare their practical implications such as sparsity and robustness. Discuss when to choose each based on feature selection needs, computational constraints, and data characteristics. Finally, explain dropout as a different regularization technique that prevents co-adaptation and can be combined with L1/L2.
Pro tip: Mention that L1 is like a Laplace prior and L2 is like a Gaussian prior, showing Bayesian understanding, and note that dropout is particularly effective in deep neural networks where L1/L2 alone may not suffice.
Explain that L1 adds the sum of absolute weights to the loss, promoting sparsity, while L2 adds the sum of squared weights, promoting small but non-zero weights.
Highlight that L1 can zero out irrelevant features (feature selection), while L2 distributes error across all weights, leading to smoother models and better handling of correlated features.
Choose L1 when interpretability and feature selection are important; choose L2 when you have many small/medium effects or correlated features; consider Elastic Net (combination) for both.
Describe dropout as randomly dropping units during training, which prevents co-adaptation and acts as an ensemble method, often used in neural networks.
Explain that dropout is complementary to L1/L2 and can be used together; L1/L2 are common in linear models, while dropout is specific to neural networks.
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Start by defining the two loss functions and their underlying probabilistic assumptions, then explain that the choice depends on the nature of the target variable and the output activation. Use concrete examples to illustrate when each is appropriate, and mention practical considerations like gradient behavior and robustness to outliers.
Pro tip: Mention that cross-entropy is the natural choice for classification because it corresponds to maximum likelihood estimation for categorical distributions, while MSE is for regression under Gaussian noise assumptions. Also note that using MSE with sigmoid/softmax can lead to vanishing gradients, which is a common pitfall.
Briefly explain that cross-entropy measures the difference between two probability distributions and is used for classification, while MSE measures the average squared difference between predictions and targets and is used for regression.
Explain that cross-entropy pairs with sigmoid (binary) or softmax (multiclass) outputs, while MSE typically pairs with linear outputs. Using the wrong combination can cause training issues like slow convergence.
Highlight that cross-entropy arises from maximum likelihood estimation for Bernoulli/Categorical distributions, while MSE arises from Gaussian noise assumptions. This justifies why each is used in its respective domain.
Mention that cross-entropy provides stronger gradients for classification, especially when predictions are wrong, while MSE is sensitive to outliers in regression. Also note that MSE can be used for classification but often performs worse.
Conclude that the choice is driven by the problem type: cross-entropy for classification (discrete targets), MSE for regression (continuous targets), and sometimes other losses like MAE for robustness.
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Started with precision/recall/F1 and AUC for classification, RMSE and MAE for regression.
Start by clearly defining the most common evaluation metrics for classification and regression, explaining what each measures and when it is appropriate. Then, describe a systematic decision-making process that ties metric selection to the business problem, data characteristics, and model trade-offs. Use concrete examples to illustrate how the choice of metric impacts model behavior and outcomes.
Pro tip: Emphasize that the right metric aligns with the business objective and the cost of different error types—this shows you think beyond technical correctness. Mention that at companies like Meta, metrics are often tied to product goals (e.g., user engagement, false positive rates) and that you would validate metric choice with stakeholders.
List key classification metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC, and briefly explain what each measures and its sensitivity to class imbalance.
List key regression metrics such as MSE, RMSE, MAE, MAPE, and R-squared, and explain how they penalize errors differently and their interpretability.
Explain how to align metric choice with the problem's goal: e.g., minimize false negatives in medical diagnosis (recall), minimize false positives in spam detection (precision), or minimize large errors in price prediction (RMSE).
Discuss how class imbalance, outliers, and the need for interpretability influence metric selection, and how to use multiple metrics for a holistic view.
Describe how to validate the chosen metric with stakeholders, monitor it post-deployment, and adjust if business priorities change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said cross-validation is more useful when data is limited and you can't afford to hold out a big chunk.
Start by explaining the standard train/validation/test split and its purpose, then discuss when cross-validation is preferred, focusing on dataset size, variance, and computational trade-offs. Emphasize practical considerations like data leakage, stratification, and time-series splits.
Pro tip: Mention that for large datasets, a single validation set is often sufficient, but for small datasets, cross-validation provides more reliable performance estimates. Also, highlight that cross-validation is crucial for hyperparameter tuning to avoid overfitting to a single validation set.
Explain the typical 60/20/20 or 70/15/15 split for train/validation/test, and the role of each set: training for model fitting, validation for hyperparameter tuning and model selection, test for final unbiased evaluation.
For small datasets, a fixed split may lead to high variance in performance estimates. Cross-validation (e.g., k-fold) uses all data for training and validation, providing a more robust estimate.
Mention stratification for imbalanced classes, grouping for clustered data, and time-series splits to prevent data leakage. Cross-validation can be adapted (e.g., stratified k-fold, time-series split).
Cross-validation requires training k models, which can be computationally expensive. For large datasets or complex models, a fixed split may be more practical.
Use cross-validation when you need reliable performance estimates for model selection or hyperparameter tuning, especially with limited data. Use a fixed split when data is abundant and computational resources are limited.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by categorizing gradient descent variants into three main types: batch, stochastic, and mini-batch, then discuss advanced optimizers like Momentum, RMSProp, and Adam. Explain how learning rate scheduling (e.g., step decay, cosine annealing, warm restarts) helps balance convergence speed and stability, and relate it to practical training scenarios.
Pro tip: Emphasize that the choice of variant and schedule depends on the problem scale and hardware constraints, and mention that adaptive methods like Adam often reduce the need for manual scheduling but can still benefit from it. Also, note that Meta often deals with large-scale recommendation and vision models, so highlighting experience with distributed training and learning rate warmup can set you apart.
Briefly explain that gradient descent is an optimization algorithm to minimize a loss function by iteratively updating parameters in the direction of the negative gradient.
Describe batch gradient descent (uses entire dataset), stochastic gradient descent (SGD, uses one sample), and mini-batch gradient descent (uses a subset). Mention that mini-batch is most common in practice.
Discuss variants like Momentum, Nesterov accelerated gradient, Adagrad, RMSProp, and Adam, explaining how they adapt learning rates or add momentum to improve convergence.
Describe common schedules: step decay, exponential decay, cosine annealing, and warm restarts. Explain how they adjust the learning rate over time to avoid overshooting minima and to escape saddle points.
Discuss how a high initial learning rate can cause divergence, while a low one can slow convergence. Scheduling helps maintain stability by reducing the learning rate as training progresses, and warmup can prevent early instability.
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Start by defining learning curves and their axes, then explain how the gap between training and validation performance diagnoses underfitting vs. overfitting. Use a concrete example to illustrate the patterns and discuss how to act on each diagnosis.
Pro tip: Emphasize that learning curves are a diagnostic tool, not just a visualization—always pair them with a clear action plan (e.g., more data, regularization) to show you can drive model improvements.
Explain that learning curves plot training and validation error (or accuracy) against training set size or training iterations. This sets the foundation for interpretation.
Describe that underfitting occurs when both training and validation errors are high and converge to a similar, poor performance level. The model is too simple to capture the underlying pattern.
Describe that overfitting occurs when training error is low but validation error is high, with a large gap between the two curves. The model memorizes training data but fails to generalize.
For underfitting, suggest increasing model complexity, adding features, or reducing regularization. For overfitting, suggest getting more data, simplifying the model, or adding regularization.
Stress that learning curves should be re-plotted after each change to confirm the diagnosis and measure improvement, closing the loop on root cause analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.