[amp_mcq option1=”Upsampling” option2=”Downsampling” option3=”Feature Engineering” option4=”Data Wrangling” correct=”option1″]<\/p>\n
The correct answer is A. Upsampling.<\/p>\n
Upsampling is a technique used to increase the number of samples in a minority class in order to balance the dataset. This can be done by duplicating samples from the minority class, or by generating new samples using techniques such as synthetic minority over-sampling technique (SMOTE).<\/p>\n
Downsampling is a technique used to reduce the number of samples in a majority class in order to balance the dataset. This can be done by randomly removing samples from the majority class, or by selecting samples from the majority class using a stratified sampling method.<\/p>\n
Feature engineering is a process of creating new features from existing features in order to improve the performance of a machine learning model. This can be done by combining existing features, or by creating new features based on domain knowledge.<\/p>\n
Data wrangling is a process of cleaning and transforming data in order to make it suitable for machine learning. This can involve removing duplicate data, filling in missing values, and converting data into a format that can be used by a machine learning model.<\/p>\n
In the context of classification problems, imbalanced datasets can occur when there are a large number of samples in one class and a small number of samples in the other class. This can make it difficult for a machine learning model to learn to classify the minority class correctly. Upsampling is a common technique used to handle imbalanced datasets in classification problems. This technique involves duplicating samples from the minority class in order to increase the number of samples in that class. This can help to improve the performance of a machine learning model on the minority class.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Upsampling” option2=”Downsampling” option3=”Feature Engineering” option4=”Data Wrangling” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[734],"tags":[],"class_list":["post-51285","post","type-post","status-publish","format-standard","hentry","category-introduction-to-data-science","no-featured-image-padding"],"yoast_head":"\n