Scikit Learn, Pandas, and NumPy Usage Questions
Practical proficiency with these core libraries. Pandas: DataFrames, data manipulation, handling missing values. NumPy: arrays, vectorized operations, mathematical functions. Scikit-learn: preprocessing, model fitting, evaluation metrics, pipelines. Knowing standard patterns and APIs. Writing efficient, readable code using these libraries.
HardTechnical
63 practiced
Explain nested cross-validation and why it provides an unbiased estimate of generalization for hyperparameter selection. Using scikit-learn, provide code to run nested CV comparing RandomForestClassifier and LogisticRegression where an inner GridSearchCV tunes hyperparameters and an outer cross_val_score reports the final distribution of scores.
EasyTechnical
54 practiced
Write Python code using scikit-learn to construct a Pipeline that scales numeric features with StandardScaler, fits a LogisticRegression with L2 penalty, and uses cross_val_score to evaluate ROC AUC. Explain why using a Pipeline is important when performing cross-validation and how it prevents data leakage.
EasyTechnical
55 practiced
Explain differences between NumPy slicing (views) and advanced integer indexing (copies). Given a = np.arange(12).reshape(3,4), demonstrate a[1:3,:], a[[0,2],[1,3]], and show how assignment to these views or copies affects the original array. Provide code and explain when a copy is created.
EasyTechnical
79 practiced
In Python, using pandas and numpy, write concise code to create a DataFrame from arrays user_id=[1,2,3], signup_date=['2025-01-01','2025-01-05','2025-02-10'], value=[10.5,20.0,np.nan]; convert signup_date to datetime, set it as the index, select rows between '2025-01-01' and '2025-01-31', and fill missing values in 'value' with the column mean. Include a short comment explaining each step.
MediumSystem Design
74 practiced
Design a reproducible training script for an sklearn model that sets random seeds for numpy, python's random, and sklearn-related randomness, logs hyperparameters and metrics, saves the final pipeline with joblib, and writes metadata (git SHA, data snapshot id, package versions) to a JSON file. Sketch the file layout and provide code snippets for seeding and saving artifacts.
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