Pretty standard starting point but I underestimated how much time to budget here.
Start by loading the data and performing a high-level overview (shape, dtypes, head). Then systematically analyze distributions, missing values, and outliers, and finally visualize relationships with the target using appropriate plots. Emphasize how each step informs modeling decisions.
Pro tip: Always tie EDA findings back to modeling implications—e.g., how missing values or outliers might affect model choice or require preprocessing. This shows you think like an ML engineer, not just a data analyst.
Load the dataset (e.g., with pandas) and check its shape, data types, and first few rows. This gives a quick sense of the data structure and potential issues.
Compute summary statistics (mean, median, std, etc.) for numerical features and value counts for categorical features. Identify missing values per column and their patterns.
Use box plots, IQR, or z-scores to detect outliers in numerical features. Investigate whether outliers are errors or valid extreme values, and consider their impact on modeling.
Create plots (e.g., scatter plots, box plots, correlation heatmaps) to explore how features relate to the target variable. Look for trends, separability, and potential interactions.
Summarize key findings (e.g., missing values, outliers, important features) and outline preprocessing or feature engineering steps needed before modeling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with ridge, explained the bias-variance tradeoff briefly.
Start by clarifying the problem setup and assumptions, then outline a complete pipeline from data splitting to model evaluation. Emphasize the importance of regularization, proper preprocessing, and hyperparameter tuning via cross-validation, and discuss how to interpret the results in a trading context.
Pro tip: Always use a time-based split for financial data to avoid lookahead bias, and consider using a validation set for hyperparameter tuning to keep the test set truly held out.
Ask about the dataset, target variable, and any domain-specific constraints (e.g., time series, high dimensionality). Confirm the evaluation metric and whether a time-based split is needed.
Standardize features if using regularization, handle missing values, and split into training and test sets (respecting temporal order if applicable). Optionally create a validation set for tuning.
Choose between Ridge (L2) and Lasso (L1) based on feature selection needs. Implement using a library like scikit-learn, ensuring the intercept is not regularized.
Use cross-validation (e.g., TimeSeriesSplit for temporal data) to select the regularization strength (alpha). Consider nested CV if the dataset is small.
Fit the final model on the training set and evaluate on the held-out test set using appropriate metrics (e.g., MSE, R²). Interpret coefficients and discuss trade-offs (bias-variance, feature selection).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the target variable and data characteristics (e.g., regression vs. classification, dataset size) to justify your choices. Then walk through the architecture, loss function, optimizer, and learning rate selection, explaining the reasoning behind each. Finally, describe the training loop, validation, and potential hyperparameter tuning.
Pro tip: Demonstrate awareness of the bias-variance trade-off by starting with a simple model and gradually increasing complexity, and mention that you would monitor training and validation loss to detect overfitting early.
Ask about the target variable type (continuous or categorical), dataset size, and feature dimensionality to determine the appropriate loss function and model capacity.
Propose a small feedforward network with 1-2 hidden layers, choosing activation functions (e.g., ReLU for hidden layers) and output units based on the target.
Choose loss (e.g., MSE for regression, cross-entropy for classification), optimizer (e.g., Adam), and a reasonable initial learning rate (e.g., 0.001), with justification.
Describe the training loop: batch size, number of epochs, validation split, and early stopping. Mention monitoring metrics and potential hyperparameter tuning.
Explain how you would evaluate the model (e.g., hold-out test set) and iterate on architecture or hyperparameters based on performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining how to diagnose overfitting from training and validation curves, then describe at least two techniques to mitigate it, and finally discuss the trade-offs of each technique in terms of bias-variance, computational cost, and business impact. Use a concrete example to illustrate your reasoning and show how you would validate the chosen approach.
Pro tip: Quantify the trade-offs whenever possible—for example, mention that regularization might reduce validation error by X% at the cost of increased training time—and relate them to business metrics like latency or revenue impact.
Describe the typical pattern: training loss continues to decrease while validation loss starts to increase, indicating the model is memorizing noise. Mention that the gap between training and validation performance widens over epochs.
Choose at least two techniques such as regularization (L1/L2, dropout), early stopping, data augmentation, or reducing model complexity. Explain briefly how each works.
For each technique, discuss trade-offs: e.g., regularization may improve generalization but can underfit if too strong; early stopping saves computation but may stop before optimal; data augmentation increases data diversity but adds preprocessing overhead.
Describe how you would validate the chosen approach using a hold-out set or cross-validation, and how you would monitor for overfitting/underfitting after applying the techniques.
Connect the technical trade-offs to business implications, such as model latency, interpretability, or development time, showing awareness of the company's priorities.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.