Which of the following is not a data reduction technique?a.Feature creationb.samplingc.Normalizationd.Dimensionality reduction
Question
Which of the following is not a data reduction technique?a.Feature creationb.samplingc.Normalizationd.Dimensionality reduction
Solution
The option that is not a data reduction technique is c. Normalization.
Normalization is a data preparation technique, not a data reduction technique. It is used to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values or losing information.
On the other hand, feature creation, sampling, and dimensionality reduction are all data reduction techniques.
- Feature creation is a process of creating meaningful new variables by transforming, aggregating, or interacting two or more variables.
- Sampling is a method used to select a subset of the population in order to infer about the population as a whole.
- Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables.
Similar Questions
Which of the following is an example of a dimensionality reduction technique?Question 6Answera.Support vector machine (SVM)b.Principal component analysis (PCA)c.AdaBoostd.K-nearest neighbors (KNN)
.Dimension reduction falls under which data pre-processing type? A. Data Cleaning B. Data Integration C. Data Reduction D. Data Transformation
n unsupervised learning, which of the following methods can be used for dimensionality reduction?a)Principal Component Analysis (PCA)b)Random Forestc)AdaBoostd)Gradient Boosting
Which of the following is a method used for data cleaning?a. Data miningb. Data filteringc. Data encryptiond. Data scaling
Which of the following techniques would perform better for reducing dimensions of a data set?(1 Point)removing columns which have too many missing valuesremoving columns which have high variance in dataremoving columns with dissimilar data trendsnone of these
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.