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moodle.spit.ac.in
Machine Learning-25-26

Machine Learning-25-26

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If you use an SVM to predict a continuous value (e.g., stock

price) instead of a class, you are using a variation called:

Regression (SVR)

Multi-class SVM

Support Vector Classification (SVC)

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In a decision tree used for

classification, what is the primary goal when choosing a feature for a

split at any node?

To select the feature with the

fewest unique values.

To increase the depth of the tree as much as

possible.

To create child nodes that are as pure as possible (i.e.,

containing samples from a single class).

To create child nodes that are as mixed and

diverse as possible.

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Which of the following is a popular choice for a

kernel function when dealing with complex, non-linear data in an SVM?

Function Kernel

Radial Basis Function (RBF) Kernel

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Which of the following is an example of an

unsupervised learning task?

Grouping customers into different market segments based on their

purchasing behavior.

Filtering emails as “spam” or “not spam”.

A self-driving car learning to apply

brakes at a red light.

Predicting whether a patient has a certain disease

based on their medical history.

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The CART (Classification and Regression Trees)

algorithm is characterized by creating which type of splits?

Splits based only on categorical features

Random splits based on a

probability distribution

Multi-way splits (a

node can split into more than two branches)

splits (each node splits into exactly two branches)

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In the context of machine learning, what are

“features”?

The output or prediction of the model.

The performance metric used to

evaluate the model.

The algorithm used to train the model.

The individual,

measurable properties or characteristics of the data being observed.

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A real estate company wants to predict the

future sale price of a house based on features like area, number of

bedrooms, and location. This is a classic example of what type of

machine learning problem?

Reinforcement Learning

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A key difference between Bagging and

Boosting is that:

Bagging focuses on reducing bias, while Boosting

focuses on reducing variance.

Bagging can only be used with

decision trees, while Boosting can use any model.

Bagging trains models in parallel, while

Boosting trains them sequentially.

unsupervised method, while Boosting is supervised.

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The ROC (Receiver Operating Characteristic) curve is a

plot of:

True Positive Rate vs. True Negative

Rate

True Positive Rate vs. False Positive Rate

Accuracy vs. Number of

features

Precision vs. Recall

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Principal Component Analysis (PCA) is a technique

used for:

Classifying data into predefined categories.

Reducing the dimensionality (number of features) of a dataset.

continuous target value.

Grouping similar data points together.

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