Machine Learning Lab Course Materials & Test Answers | moodle.spit.ac.in

Which model is most interpretable?

Decision Tree

✅

KNN

❌

SVM

❌

Deep Learning

❌

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Which model is non-parametric?

KNN

0%

Logistic Regression

0%

Linear Regression

❌

SVM (linear)

0%

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Feature scaling is important in SVM because:

It removes noise

❌

It increases features

❌

It reduces labels

❌

Distance calculation depends on scale

✅

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SVM works well in:

Sequential data only

❌

Low memory only

❌

Small trees

❌

High-dimensional spaces

✅

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Common kernel used in SVM:

RBF Kernel

✅

Gini Index

❌

Entropy

❌

Mean Squared Error

❌

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SVM tries to maximize:

Number of trees

❌

Clusters

❌

Margin between classes

✅

Accuracy

❌

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If K is too small, the model may:

Overfit

0%

Underfit

❌

Be stable

0%

Ignore data

0%

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Main hyperparameter in KNN is:

Kernel

❌

Number of neighbors (K)

✅

Learning rate

❌

Depth

❌

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KNN commonly uses which distance metric?

Euclidean Distance

✅

Log Loss

❌

Accuracy

❌

Entropy

❌

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Linear Regression is mainly used for:

Dimensionality reduction

❌

Predicting continuous values

✅

Clustering

❌

Classification problems

❌

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Machine Learning Lab

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