---
title: "Classification and prediction with stages"
subject: "Data Mining"
chapter: 3
key: "semester_7/data_mining:classification-stages"
source: https://www.bctengineer.com/q/semester_7/data_mining/classification-stages
site: BCT Engineering
---

# Classification and prediction with stages

Data Mining · Chapter 3: Classification

- Asked in: year not given
- Video: [#17 Classification & Prediction - Example, Steps |DM|](https://www.youtube.com/watch?v=erm7qdVCEgU) — Trouble- Free
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/classification-stages
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-3

## Question

Define Classification and prediction with example. Explain different stages in Classification with clear block diagram.

## Short answer

Classification predicts discrete class labels (e.g., loan safe/risky) from labeled training data; prediction estimates continuous values. Stages: data preparation, model construction using a learning algorithm, model evaluation on a test set, and model usage to classify new data.

## Answer

**Classification** is a supervised learning task that predicts a discrete class label for an object using a model learnt from training data with known labels. *Example*: classifying a loan applicant as "safe" or "risky".

**Prediction** estimates a continuous or missing numeric value (regression). *Example*: predicting how much a customer will spend on a computer. (Some authors use "prediction" for both.)

#### Stages of classification
1. **Data preparation**: clean the data, select relevant attributes, normalise and discretize, and divide the data into a training set and a test set.
2. **Model construction (learning step)**: a classification algorithm (decision tree, Naive Bayes, neural network) analyses the training tuples, whose class labels are given, and builds a model such as rules, a tree or a formula.
3. **Model evaluation**: the model is applied to the independent test set, and accuracy = correctly classified/total is found. If it is not acceptable, the model is rebuilt with other parameters or algorithms.
4. **Model usage (classification step)**: the accepted model classifies new unseen tuples.

```text
Training data --> [Learning algorithm] --> Model
                                            |
Test data ------> [Evaluate accuracy] <-----+
                          | acceptable?
New data  ------> [Use the model] ---> predicted class
```

*Example*: Training tuples (name, rank, years, tenured) are used to build the rule IF rank = professor OR years > 6 THEN tenured = yes. After the test accuracy is found, the rule classifies the new tuple (Jeff, professor, 4).

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. What distinguishes classification from prediction?
   A. Classification is unsupervised
   B. Prediction uses labels, classification doesn't
   C. Classification predicts a discrete class label; prediction estimates a continuous value
   D. They are identical always
   Answer: C. Loan safe/risky vs spending amount. (Key point: Definition)

2. What happens in model construction?
   A. The model is tested on new data
   B. Data are clustered without labels
   C. Accuracy is computed on training data only
   D. An algorithm analyses labelled training tuples and builds a model
   Answer: D. Learning uses training tuples with known labels. (Key point: Learning step)

3. Why is a separate test set used?
   A. To estimate accuracy on independent data
   B. To train the model
   C. To select attributes
   D. To normalise data
   Answer: A. Accuracy = correctly classified / total on test data. (Key point: Evaluation)

4. When is the model used to classify new tuples?
   A. Before training
   B. After its accuracy is found acceptable
   C. Before data preparation
   D. Only if accuracy is zero
   Answer: B. If unacceptable, it is rebuilt. (Key point: Usage)

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