DAC Machine Learning & Predictive Analytics 1

Question 1

Which machine learning algorithm is best suited for classifying emails as spam or not spam?

Accepted Answer

Naïve Bayes

Answer

Naïve Bayes is a probabilistic machine learning algorithm based on Bayes' theorem, commonly used for classification tasks, especially text classification. It is highly effective for classifying emails as spam or not spam because it can quickly learn from the presence or absence of specific words and features within an email. Its simplicity and efficiency make it a popular choice for this application.

Question 2

What is the purpose of cross-validation in machine learning?

Accepted Answer

To prevent overfitting

Answer

Cross-validation is a crucial technique in machine learning used to assess how well a model generalizes to new, unseen data. By training and testing the model on different subsets of the data, it helps detect and prevent overfitting. Overfitting occurs when a model learns the training data too well, including noise, and performs poorly on new data.

Question 3

Which metric is commonly used to evaluate the performance of a regression model?

Accepted Answer

Mean Squared Error (MSE)

Answer

Mean Squared Error (MSE) is a widely used metric to evaluate the performance of regression models. It calculates the average of the squared differences between the predicted values and the actual values. MSE quantifies the average magnitude of the errors, with lower values indicating a better fit of the model to the data.

Question 4

What is the main goal of feature scaling in machine learning?

Accepted Answer

To make numerical features comparable

Answer

The main goal of feature scaling in machine learning is to transform numerical features so they have a similar range or distribution. This prevents features with larger values from dominating the learning process, ensuring that all features contribute equally to the model's performance. It is particularly important for algorithms sensitive to feature magnitudes, such as K-Nearest Neighbors or Support Vector Machines.

Question 5

Which of the following is an unsupervised learning algorithm?

Accepted Answer

K-Means Clustering

Answer

K-Means Clustering is an unsupervised learning algorithm that groups data points into 'k' clusters based on their similarity. Unlike supervised learning, it does not require labeled data, making it ideal for discovering inherent structures or patterns within a dataset. It aims to partition data into clusters where each data point belongs to the cluster with the nearest mean.

Data Analytics Certifications (DAC) Practice Test

Data Analytics Certifications (DAC) Practice Test

DAC Machine Learning & Predictive Analytics 1