Train and improve a scikit-learn binary classifier

Q: Train and improve a scikit-learn binary classifier

Evaluates the ability to train, evaluate, and iteratively improve a scikit-learn binary classifier, encompassing model selection, preprocessing, validation practices, handling class imbalance, and interpretation of performance metrics.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Q: What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Perplexity.

Q: What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Perplexity during technical interviews.

Question

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Practical ML fundamentals (Python + scikit-learn)

You are given a small toy binary-classification dataset (e.g., arrays/dataframes X_train, y_train, X_valid, y_valid or a single dataset you must split). Your task is to:

Train a baseline binary classifier using scikit-learn .
- Choose a reasonable model (e.g., logistic regression, linear SVM, random forest, gradient boosting).
- Fit it on the training set.
Evaluate the model on the validation set using one or more evaluation metrics.
- Common choices: accuracy, precision/recall, F1, ROC-AUC, PR-AUC, confusion matrix.
After you see the initial metric(s), improve the evaluation metric(s) .
- You may change the model, tune hyperparameters, adjust preprocessing, address class imbalance, change decision thresholds, or revise the validation approach.

Constraints / expectations

Use Python and scikit-learn APIs.
Keep the solution clean and reproducible (e.g., use Pipeline , set random_state , avoid data leakage).
Explain your choices and how each change is expected to affect the metric.

Train and improve a scikit-learn binary classifier

Quick Overview

Practical ML fundamentals (Python + scikit-learn)

Constraints / expectations

Solution

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