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8. Which component in Orange Data Mining tool enables users to evaluate the performance of machine
learning models?
Ans. Test and Score widget enables users to evaluate the performance of machine learning models in Orange
Data Mining tool.

E. Short answer type questions.
1. Why is data visualisation crucial in Data Science?

Ans. Data visualisation helps communicate complex insights clearly, making it easier for stakeholders to
understand and make informed decisions.

2. How can you handle empty values in a dataset, and why is it necessary to address them before training
an AI model?
Ans. Empty values can be deleted or filled, and addressing them is essential to prevent inaccurate predictions
and biased outcomes in AI models.

3. In Data Science, what does the term ‘feature’ refer to?
Ans. A ‘feature’ is a measurable property or characteristic of data used to train machine learning models and

make predictions.
4. Define No-Code and Low-Code AI.

Ans. No-Code AI enables non-technical users to build and deploy AI applications using visual interfaces and
pre-built components, while Low-Code AI allows users with some coding knowledge to develop AI
solutions with reduced coding requirements and visual tools.
5. Write a short note on Descriptive Statistics.

Ans. Descriptive statistics refers to the process of summarising numerical and categorical data in a concise
and informative manner. It involves using various measures, such as mean, median, mode, standard
deviation, and variance.

6. Explain the following widgets in Orange Data Mining platform:
a. Test and Score b. Confusion Matrix

c. Impute d. Predicti ons

Ans: a. Test and Score: This widget is used to evaluate the performance of a predictive model on a test
dataset.
b. Confusion Matrix: This widget provides a visual and numerical representation of the model's

performance by comparing the predicted classifications with the actual classifications.
c. Impute: This widget addresses missing values in the dataset.
d. Predictions: This widget is a crucial tool for making and visualising predictions based on trained

machine learning models. It enables users to apply their models to new data and analyse the
results, making it an essential part of the workflow for any predictive analysis.
7. Differentiate between the following:

a. Modelling widgets and Evaluati on widgets

b. Feature Selecti on widgets and Visualisati on widgets

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