Machine Learning Quiz

What is the primary difference between traditional programming and machine learning?

• Algorithm learns rules from data

• Input + Rules = Output

• Programmer defines rules explicitly

• Data-driven system: Input + Output = Learns rules

Which step involves cleaning and preprocessing data in the machine learning process?

• Data Preparation

• Model Selection

• Data Collection

• Evaluation

What is the purpose of feature selection in machine learning?

• To increase data size

• To improve model complexity

• To create new features

• To choose the most relevant variables

Which of the following is an example of a supervised learning algorithm?

• DBSCAN

• Principal Component Analysis

• Linear Regression

• K-Means Clustering

What is the main goal of unsupervised learning?

• To optimize model parameters

• To classify data into predefined categories

• To find hidden patterns in unlabeled data

• To predict outcomes based on labeled data

In the context of machine learning, what does 'overfitting' refer to?

• Model performs well on training data but poorly on validation data

• Model is too simple to capture data complexity

• Model fails to learn underlying patterns

• Model learns noise and performs poorly on new data

Which of the following is NOT a key application of machine learning in healthcare?

• Drug Discovery

• Personalized Medicine

• Fraud Detection

• Disease Diagnosis

What is the purpose of k-Fold Cross Validation?

• To split data into training and testing sets

• To assess model performance on different data subsets

• To increase the size of the training dataset

• To tune hyperparameters

Which method is used to evaluate the importance of each feature independently in feature selection?

• Correlation Methods

• Embedded Methods

• Wrapper Methods

• Filter Methods

What is a common pitfall to avoid in feature selection?

• Including irrelevant features

• Using too few features

• Selecting features before training

• Ignoring domain knowledge