[amp_mcq option1=”Data preprocessing” option2=”Model evaluation” option3=”Feature selection” option4=”Hyperparameter tuning” correct=”option3″]<\/p>\n
The correct answer is C. Feature selection.<\/p>\n
Feature selection is the process of selecting a subset of features from the original set of features that are most relevant to the target variable. This can be done using a variety of methods, such as filter methods, wrapper methods, and embedded methods.<\/p>\n
Filter methods select features based on their individual importance, without considering the relationship between the features. Wrapper methods select features by evaluating the performance of a model on a holdout set, while varying the set of features that are used. Embedded methods select features by jointly optimizing the model’s performance and the feature selection process.<\/p>\n
Feature selection is an important step in machine learning, as it can improve the performance of the model by reducing the dimensionality of the data and by removing irrelevant features.<\/p>\n
A. Data preprocessing is the process of cleaning and transforming the data so that it can be used by a machine learning model. This can involve removing duplicate data, filling in missing values, and normalizing the data.<\/p>\n
B. Model evaluation is the process of assessing the performance of a machine learning model. This can be done by comparing the model’s predictions to the actual values, or by using a metric such as accuracy or precision.<\/p>\n
D. Hyperparameter tuning is the process of finding the optimal values for the hyperparameters of a machine learning model. Hyperparameters are the parameters that control the learning process, such as the number of iterations or the learning rate.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Data preprocessing” option2=”Model evaluation” option3=”Feature selection” option4=”Hyperparameter tuning” correct=”option3″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[738],"tags":[],"class_list":["post-44858","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n