1. What is the main goal of machine learning?
Explanation ≡
Correct answer: option cThe main goal of machine learning is to develop algorithms and models that can learn from data and improve their performance on specific tasks without being explicitly programmed.
2. What is the purpose of the training set in machine learning?
Explanation ≡
Correct answer: option dThe training set is used to teach the machine learning model by providing labeled examples. The model learns patterns and relationships in the data to make accurate predictions on new, and unseen data.
3. You are given reviews of a few movies marked as positive, negative, or neutral. Classifying reviews of a new movie is an example of
Explanation ≡
Correct answer: option dSupervised learning is defined as learning from labeled examples. Given labeled reviews, the model can learn from them and predict new examples.
4. In machine learning, what is the definition of a feature?
Explanation ≡
Correct answer: option aIn machine learning, a feature refers to a measurable or observable characteristic of an input data point. Features are used to represent the data and provide information that helps the machine learning algorithm understand and make predictions or classifications. Features can be numerical, categorical, or binary and are typically represented as columns or variables in a dataset.
5. What is the goal of feature selection in machine learning?
Explanation ≡
Correct answer: option cFeature selection aims to identify and select the most informative features from the dataset, thereby reducing the dimensionality. It helps in improving model performance, reducing computational complexity, and avoiding the curse of dimensionality.
6. What is the purpose of feature scaling in machine learning?
Explanation ≡
Correct answer: option cFeature scaling is used to ensure that all features or variables in the dataset are on a similar scale. This is important because some machine learning algorithms are sensitive to the scale of features. Scaling the features helps in achieving faster convergence, preventing dominant features from overshadowing others, and ensuring the algorithm treats all features equally.
7. Which of the following is a categorical feature?
Explanation ≡
Correct answer: option cCategorical variables represent types of data that may be divided into groups. All other features are continuous.
8. What is the primary goal of supervised learning?
Explanation ≡
Correct answer: option cThe primary goal of supervised learning is to learn from labeled examples, where the input data is paired with corresponding output labels. The model is trained to make predictions or decisions on unseen data based on the patterns learned from the labeled examples.
9. Which of the following task(s) can be solved using unsupervised learning?
Explanation ≡
Correct Answers: options b, cUnsupervised learning is used for tasks like customer segmentation, where patterns or clusters are identified in the data, and anomaly detection, where abnormal data points are identified.
10. Which of the following is a classification task?
Explanation ≡
Correct answer: option dClassification involves predicting a discrete category or class. In this case, predicting whether there will be abnormally heavy rainfall is a classification task as it involves making a binary (yes/no) decision.
11. Which of the following statement(s) is/are true about unsupervised learning in machine learning?
Explanation ≡
Correct Answers: options b, c, eUnsupervised learning is a type of machine learning where the algorithm learns from unlabeled data without any specific target variable or labels. The goal is to discover patterns, structures, or relationships within the data.
12. Which of the following statement(s) is/are true about supervised learning in machine learning?
Explanation ≡
Correct Answers: options a, c, dSupervised learning is a type of machine learning where the algorithm learns from labeled training data, where each data instance has corresponding input features and output labels.
13. Which of the following technique(s) is/are used for handling missing data in machine learning?
Explanation ≡
Correct Answers: options a, b, dMissing data is a common issue in real-world datasets, and various techniques can be used to handle it. Removing the rows with missing data is one approach, but it may result in a loss of information if the missing data is substantial. Imputing the missing values with mean, median, or mode is a common technique where the missing values are replaced with the central tendency of the corresponding feature. Using regression models to predict missing values is another approach where the model is trained using the available data and used to predict the missing values. Ignoring the missing data during training is generally not recommended as it can lead to biased or incomplete models.
14. Which of the following is NOT a type of machine learning algorithm?
Explanation ≡
Correct answer: option dData preprocessing is not a type of machine learning algorithm. It refers to the steps taken to prepare and clean the data before applying machine learning techniques.
15. Which of the following is NOT a common evaluation metric used in machine learning?
Explanation ≡
Correct answer: option dAccuracy, precision, and recall are commonly used evaluation metrics in machine learning. Accuracy measures the overall correctness of the model's predictions. Precision measures the proportion of true positive predictions out of all positive predictions, while recall measures the proportion of true positive predictions out of all actual positive instances. These metrics are used to assess the performance of classification models. On the other hand, profit margin is not a direct evaluation metric used in machine learning. Profit margin is a business metric that relates to the financial performance of a company and is not specifically tied to model evaluation. While machine learning models can be used to optimize business decisions and improve profitability, the evaluation metrics for the models are typically focused on their predictive performance rather than financial metrics like profit margin.
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