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8. Observe the scatter plot showing the amount of sleep needed per day by age. [CBSE Handbook]
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What type of correlation is shown here?
Ans. As age increases (moving along the x-axis toward greater numbers), the amount of sleep needed decreases (y-values
decreasing). This is a negative correlation. This indicates that as individuals grow older, they generally require less sleep.
9. Write down any 4 limitations of KNN.
Ans. Some limitations of KNN are as follows:
• Appropriate k selection is crucial in KNN since it has a significant impact on model accuracy, requiring careful tuning.
• KNN has issues with imbalanced datasets, that are biased towards the dominant class and generate incorrect
projections for minority classes.
• KNN evaluation stage might be highly computational because it calculates distance for every training occurrence.
• KNN retains every piece of training data, requiring large memory resources.
10. What do you understand by Centroid?
Ans. A centroid is an imaginary or real location denoting the centre of the cluster. The K-means algorithm identifies K
number of centroids, and then assigns every data point to the nearest cluster, while trying to keep the centroids as
small as possible.
B. Long answer type questions:
1. Define the following terms and give examples from real life:
a. Causation
b. Outlier
Ans: a. Causation: Causation shows that an event is the direct result of the occurrence of another event, i.e. a causal
relationship exists between the two events. This is also called cause and effect. For example, a speeding car leads
to an accident. The accident is due to causation.
b. Outlier: In statistics, outliers are data points that are significantly different from other observations. Outliers may
be due to measurement irregularity or may indicate experimental error; the latter are sometimes excluded from the
data set. Outliers can cause serious problems in statistical analysis.
2. Explain Classification with example of supervised learning.
Ans. In Artificial Intelligence, classification is the process of labelling a set of data (structured or unstructured). into different
classes or groups where we can assign a label to each class. In machine learning, a predictive classification model tries
to approximately map the function from input variables to discrete output variables. The main goal is to determine
which class/category the new data will belong to. For example, heart disease detection is a classification problem. There
are only two classes in this case—a patient has heart disease or does not have heart disease. In this case, the classifier
needs training data to understand how a given input variable is related to the class. Once the classifier is properly
trained, it can be used to determine whether a particular patient has heart disease or not.
Machine Learning Algorithms 357

