Machine Learning Quiz2

What is the use of Validation dataset in Machine Learning?

• To train the machine learning model.

• To evaluate the performance of the machine learning model

• To tune the hyperparameters of the machine learning model

• None of the above.

In a recent project, Divya was tasked with evaluating the performance of a machine learning model. Which of the following metrics should she consider?

• Precision

• Normalization

• Overfitting

• Feature construction

Aanya is analyzing the results of her machine learning model. What does the confusion matrix represent?

• The types of machine learning

• The relationship between features

• The performance of a classification model

• The training process

What is the primary purpose of a decision tree in machine learning?

• Data visualization

• Classification and regression

• Clustering

• Dimensionality reduction

What is the primary purpose of using SVM in machine learning?

• To reduce dimensionality of data

• To classify data into different categories

• To visualize data in a lower-dimensional space

• To perform regression analysis

What is the primary consequence of underfitting in machine learning?

• When a model is too complex

• When a model fails to capture the underlying trend of the data

• When the model is trained on too little data

• When a model performs well on unseen data but poorly on training data

Which of the following is a distance-based model?

• Decision Trees

• Nearest Neighbors classification

• Logistic Regression

• Naïve Bayes Classifier

What is the primary goal of supervised learning?

• To find hidden patterns in data

• To predict outcomes based on labeled data

• To cluster similar data points

• To reduce dimensionality of data

Which algorithm is commonly used for regression tasks?

• Support Vector Machines

• Linear Regression

• K-Means Clustering

• Random Forest

What does cross-validation help to achieve?

• To increase the size of the training set

• To assess the generalization ability of a model

• To optimize hyperparameters

• To visualize data distributions