Question16 What is the best approach to solve this problem:Given a website visiting activity dataset with a record of confirmed buyers, find whether a new visiter is a casual browser or a serious buyer. Regression Clustering Classification ResetMaximum marks: 0.5 Flag question undefined单项选择题
A
Regression
B
Clustering
C
Classification

Question

Question16 What is the best approach to solve this problem:Given a website visiting activity dataset with a record of confirmed buyers, find whether a new visiter is a casual browser or a serious buyer. Regression Clustering Classification ResetMaximum marks: 0.5 Flag question undefined单项选择题

A

Regression

B

Clustering

C

Classification

BlackTom AI · Accepted Answer

Binary outcome (casual browser vs serious buyer) maps directly to a supervised classification problem because the target is a discrete label and you have historical records of confirmed buyers.

1) Relevant concepts:
- Supervised learning: uses labeled examples (confirmed buyer or not) to learn mapping from features (page views, time on site, referral source) to class labels.
- Classification vs regression vs clustering: classification predicts discrete categories; regression predicts continuous values; clustering groups unlabeled data.
- Evaluation metrics: accuracy, precision, recall, F1, ROC-AUC; calibration if you want probability estimates.

2) Step-by-step reasoning:
- Step 1: Identify target variable. Historical records mark visitors as “buyer” or “non-buyer” → categorical label (binary).
- Step 2: Determine supervision. Labels exist → supervised method applicable.
- Step 3: Choose model family. Binary classification models (logistic regression, decision trees, random forests, gradient boosting, SVM, neural nets) are appropriate. If you want a probability that a new visitor will buy, many classifiers (e.g., logistic regression) naturally provide probability estimates.
- Step 4: Train/test split, feature engineering, hyperparameter tuning, and evaluate using classification metrics.

3) Intermediate reasoning steps (no numeric calculation required): assemble features from clickstream, split labeled data into training and test sets, train classifier, check precision/recall to balance false positives/negatives according to business needs.

4) Why classification is correct:
- The problem asks to assign one of two discrete states to a new visitor. That is exactly what classification models are designed to do. Labels are available, so supervised classification is the best approach.

5) Why other options are incorrect:
- Regression predicts continuous numeric outcomes (e.g., revenue amount), not categorical states — not a direct fit here.
- Clustering is unsupervised and groups similar visitors without using known buyer labels; it cannot leverage confirmed-buyer labels to optimize prediction for new visitors.

Conclusion: Classification is the appropriate choice, matching the provided answer: Classification.

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