Machine Learning MCQ Questions: Practice and Test Your Knowledge

Computers excel at learning and executing procedures. They are adept at following predefined steps and algorithms to perform tasks. While they can process facts, concepts, and principles to some extent, their strength lies in executing procedures efficiently and accurately. Procedures involve a series of steps or instructions that dictate how a task should be carried out, making them the most suitable domain for computer learning.
Therefore, the correct answer is Option C: procedures.

Feature scaling is performed before applying the K-Means algorithm to ensure that all features contribute equally to the distance computations. When features are on different scales, those with larger magnitudes may dominate the distance calculations, leading to biased results. By scaling the features, each feature contributes proportionally to the distance calculation, ensuring that no single feature dominates. This helps in achieving better cluster formation based on the actual distribution of data points.
Therefore, the correct answer is Option A: in distance calculation it will give the same weights for all features.

Naive Bayes algorithm assumes that all the features in a dataset are independent of each other given the class label. This is why it's termed "naive" because it simplifies the model by assuming independence between features, even though this assumption might not hold true in all cases.
Option A is incorrect because Naive Bayes does not assume that all features are equally important; it only assumes independence.
Option C is incorrect because Naive Bayes assumes feature independence but does not assume that all features are equally important.
Therefore, the correct answer is Option B: Assumes that all the features in a dataset are independent.

KDD stands for Knowledge Discovery in Databases. It represents the process of extracting useful knowledge or information from large volumes of data. This process involves various steps such as data cleaning, data preprocessing, data mining, and interpretation of the results to extract valuable insights or knowledge from the data.
Therefore, the correct answer is Option B: knowledge.

Linear Regression is a supervised machine learning algorithm. In supervised learning, the algorithm learns from labeled data, where each example in the training dataset consists of input features and their corresponding target labels. In the case of linear regression, the algorithm learns to predict a continuous target variable based on one or more input features.
Therefore, the correct answer is Option A: supervised.