Machine Learning Quiz

What is Machine learning?

• The selective acquisition of knowledge through the use of computer programs

• The selective acquisition of knowledge through the use of manual programs

• The autonomous acquisition of knowledge through the use of computer programs

• The autonomous acquisition of knowledge through the use of manual programs

K-Nearest Neighbors (KNN) is classified as what type of machine learning algorithm?

• Instance-based learning

• Parametric learning

• Non-parametric learning

• Model-based learning

Which of the following is not a supervised machine learning algorithm?

• K-means

• Naïve Bayes

• SVM for classification problems

• Decision tree

What’s the key benefit of using deep learning for tasks like recognizing images?

• They need less training data than other methods.

• They’re easier to explain and understand than other models.

• They can learn complex details from the data on their own.

• They work faster and are more efficient computationally.

Which algorithm is best suited for a binary classification problem?

• K-nearest Neighbors

• Decision Trees

• Random Forest

• Linear Regression

What is the key difference between supervised and unsupervised learning?

• Supervised learning requires labeled data, while unsupervised learning does not.

• Supervised learning predicts labels, while unsupervised learning discovers patterns.

• Supervised learning is used for classification, while unsupervised learning is used for regression.

• Supervised learning is always more accurate than unsupervised learning.

Which type of machine learning algorithm falls under the category of “unsupervised learning”?

• Linear Regression
  

• K-means Clustering

• Decision Trees
  

• Random Forest

What is overfitting in the context of machine learning?

• When a model is too simple to capture the underlying patterns.

• When a model is trained on too little data.

• When a model is too complex and requires more computational resources.

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