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

Machine Learning-25-26

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What is the main purpose of the “kernel trick” in

SVMs?

To reduce the number of features in the dataset.

To automatically find

the best value for the regularization parameter C.

the training process of a linear SVM.

To handle non-linearly separable

data by mapping it to a higher-dimensional space.

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In SVM terminology, what are the “support

vectors”?

The data points

that are misclassified by the hyperplane.

The data points that are

farthest from the decision boundary.

All the data points in the training set.

The data points that lie

closest to the decision boundary (hyperplane).

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Random Forest is an enhancement of Bagging

primarily because it introduces additional randomness by:

Also using a random

subset of features when splitting nodes in each tree.

Pruning each tree randomly.

Assigning random

weights to the final predictions.

random subset of training samples for each tree.

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The primary goal of a linear Support Vector Machine

(SVM) is to find a decision boundary, known as a hyperplane, that:

Has the maximum

possible margin between the classes.

Connects the most data points between two classes.

Minimizes the number of support

vectors.

Passes through the origin of the feature space.

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The primary goal of PCA is to find a new set of

features (principal components) that:

Are perfectly correlated with the original features.

the distance between data points.

Maximize the variance in the

data.

Maximize the number of clusters.

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What is the core idea behind the “Bagging” (Bootstrap

Aggregating) ensemble method?

Training multiple models

in parallel on different random subsets of the training data.

weights to different models based on their individual performance.

Training models sequentially, with each

one correcting the errors of the previous one.

Using a single deep decision tree.

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In the K-Means algorithm, a “centroid” represents:

The initial starting point for the

algorithm.

The geometric center or

mean of all the data points belonging to a cluster.

An outlier that

does not belong to any cluster.

single data point on the edge of a cluster.

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What does the F1-Score represent?

The rate of true negative predictions.

harmonic mean of precision and recall, providing a balance between them.

accuracy and precision.

The total number of correct predictions.

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The purpose of K-fold cross-validation is to:

Create an ensemble of models.

Get a more reliable estimate of a model’s performance on unseen data.

Reduce the number of features in

the data.

Speed up the model training process.

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When a machine learning model performs very well on

the data it was trained on but poorly on new, unseen data, this

situation is known as:

Dimensionality

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