Question

What is Train-Test Split Evaluation? What are the two types of problems for which Train-Test Split Evaluation can be used?

Hint:

Tip: Use Train-Test Split for a quick check, Cross Validation for a more robust evaluation.

Answer 1

Train-Test Split Evaluation is a simple and quick technique used to measure the performance of a machine learning model.
In this method, the dataset is divided into two separate parts: training data and testing data.
The model is trained on the training set and then evaluated on the unseen test set to check how well it predicts new data.
This approach helps detect overfitting by comparing performance on training vs. test data.
Train-Test Split is easy to implement and works well for large datasets where splitting does not reduce data variety too much.
Two types of problems where Train-Test Split Evaluation can be used are:
1. Classification Problems — where the goal is to assign labels or categories to input data.
For example: spam detection, image classification.
2. Regression Problems — where the goal is to predict continuous numeric values.
For example: predicting house prices, temperature forecasting.
Train-Test Split gives a quick check of model performance but can be less reliable than cross-validation for small datasets.