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.

Question

Loading...

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

Practical ML fundamentals (Python + scikit-learn)

Constraints / expectations

Solution

Comments (0)

Train and improve a scikit-learn binary classifier

Overview

Practical ML fundamentals (Python + scikit-learn)

Constraints / expectations

Solution

Comments (0)