Machine Learning

Correct Answer: Cross-validation is a technique for estimating the performance of a machine learning model by training it on several subsets of the available input data and evaluating each on the complementary subset of the data.

Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves splitting the available data into two or more subsets, using one subset to train the model and the other subset to test its performance. This procedure is repeated several times, with different subsets of the data used for training and testing, and the results are averaged to give an estimate of the model's performance on unseen data. Cross-validation can help to prevent overfitting and give a more reliable estimate of the model's performance.

Correct Answer: Precision refers to the number of true positives divided by the number of true positives and false positives, while recall refers to the number of true positives divided by the number of true positives and false negatives.

Precision measures the proportion of true positive predictions among all positive predictions, while recall measures the proportion of true positive predictions among all actual positive instances. High precision but low recall means few false positives but many false negatives (i.e., many actual positives have been incorrectly predicted as negatives). High recall but low precision means many false positives but few false negatives (i.e., there are many false alarms, but most of the actual positives are captured).

Correct Answer: To reduce the number of features while retaining as much information as possible

Dimensionality reduction is used to reduce the number of features while retaining as much information as possible. This is done to simplify the model, reduce overfitting, and improve computational efficiency. There are two main approaches to dimensionality reduction: feature selection and feature extraction. Feature selection involves selecting a subset of the original features, while feature extraction involves transforming the original features into a lower-dimensional space.