FREE DMC Machine Learning & Algorithms Questions and Answers

Question 1

What is the difference between supervised and unsupervised learning?

Accepted Answer

Supervised learning uses labeled data, unsupervised learning uses unlabeled data

Answer

Supervised learning uses unlabeled data.

Answer

Unsupervised learning uses labeled data.

Answer

Both supervised and unsupervised learning use labeled data.

Question 2

Which machine learning algorithm is used for classification tasks?

Accepted Answer

Logistic regression

Answer

Linear regression.

Answer

K-means clustering.

Answer

Principal component analysis.

Question 3

What is the purpose of decision trees in machine learning?

Accepted Answer

To classify data based on input features

Answer

To perform linear regression.

Answer

To reduce dimensionality.

Answer

To minimize errors.

Question 4

Why is cross-validation used in machine learning?

Accepted Answer

To evaluate model performance and avoid overfitting

Answer

To train models faster.

Answer

To reduce the number of features.

Answer

To remove outliers from the data.

Question 5

What is the purpose of gradient descent in machine learning?

Accepted Answer

To minimize the loss function by updating model parameters

Answer

To increase the number of features.

Answer

To split the dataset into training and test sets.

Answer

To handle missing data.

Question 6

What is the function of support vector machines in machine learning?

Accepted Answer

To separate data into distinct classes using hyperplanes

Answer

To perform linear regression.

Answer

To perform unsupervised learning.

Answer

To reduce the number of features.

Question 7

What is overfitting in machine learning?

Accepted Answer

When a model performs well on training data but poorly on new data

Answer

When a model performs poorly on both training and test data.

Answer

When the model does not learn anything from the training data.

Answer

When the model has too few parameters.

Question 8

Why is data preprocessing important in machine learning?

Accepted Answer

To normalize, clean, and transform data to improve model performance

Answer

To remove unnecessary features.

Answer

To reduce the data size.

Answer

To make data available for real-time use.

Question 9

What is ensemble learning in machine learning?

Accepted Answer

Combining multiple models to improve performance

Answer

Using a single model to make predictions.

Answer

Training the model on a smaller dataset.

Answer

Using unsupervised learning techniques.