Page 358 - AI Ver 3.0 Class 11
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2. Write down any 5 key features of machine learning.
Ans. Following are the key features of machine learning:
• Machine learning interprets, analyses, and processes data to address real-world problems.
• It learns from data and enhances its performance over time.
• The technology facilitates automation and prediction based on the learned patterns.
• Machine learning is the prevailing approach in contemporary AI.
• It employs data analysis, training, and sometimes human review to refine its capabilities.
3. List any two applications of KNN.
Ans. Two applications of KNN are as follows:
• Image recognition: KNN may be used to categorise photographs depending on their attributes, such as pixel
values and colour, etc. KNN may compare the attributes of a picture with those of labelled images in the set used for
training and classify the majority of its K-Nearest Neighbors.
• Spam detection: KNN can identify spam through the comparison of new emails to a database containing both
spam and non-spam emails.
4. List down the real life applications of linear regression.
Ans. Real life applications of linear regression include:
• Prediction of product demand
• Sales forecasting
• Analysing the effect of price change of a service
• Predict the effect of fertiliser on crop yield
• Prediction of revenue through advertisements
• Predicting salary of a person based on the number of years of experience
5. What are the four types of correlation?
Ans. Four Types of correlation are:
1. Positive Correlation: Positive correlation is the relationship between two variables, in which both variables have a
linear relationship. As one variable increases/decreases, the second variable too increases/decreases. For example,
when fuel prices increase, prices of airline tickets also increase.
2. Negative Correlation: Negative correlation is the relationship between two variables, where one variable increases
as the second variable decreases, and vice versa. For example, more exercising leads to a decrease in body weight.
3. No Correlation: No correlation means that there is no relationship between two variables. If the value of a variable is
changed, another variable is not affected. For example, shirt size and monthly expense, body weight and intelligence,
etc.
4. Non-linear Correlation: A non-linear correlation is a correlation in which all the points of a scatter plot are tend to
lie near a smooth curve.
6. Differentiate between correlation and regression.
Ans. Both Correlation and regression are statistical measures used in data analysis, however they are not same. Their
differences can be seen:
Correlation Regression
It determines the strength or degree of It determines how one variable affects another
relationship between two variables. variable.
It is represented by a single value. It is represented by a regression line.
7. What is the primary difference between classification and regression? [CBSE Handbook]
Ans. Classification predicts discrete values, while regression predicts continuous values.
356 Touchpad Artificial Intelligence (Ver. 3.0)-XI

