what is Feature scaling done before applying K-Mean algorithm?
A. in distance calculation it will give the same weights for all features
B. you always get the same clusters. if you use or dont use feature scaling
C. in manhattan distance it is an important step but in euclidian it is not
D. none of these
Answer: Option A
Solution(By Examveda Team)
Feature scaling is performed before applying the K-Means algorithm to ensure that all features contribute equally to the distance computations. When features are on different scales, those with larger magnitudes may dominate the distance calculations, leading to biased results. By scaling the features, each feature contributes proportionally to the distance calculation, ensuring that no single feature dominates. This helps in achieving better cluster formation based on the actual distribution of data points.Therefore, the correct answer is Option A: in distance calculation it will give the same weights for all features.
This Question Belongs to Computer Science >> Machine Learning
In simple term, machine learning is
A. training based on historical data
B. prediction to answer a query
C. both A and B
D. automization of complex tasks
Which of the following is the best machine learning method?
A. scalable
B. accuracy
C. fast
D. all of the above
The output of training process in machine learning is
A. machine learning model
B. machine learning algorithm
C. null
D. accuracy
Application of machine learning methods to large databases is called
A. data mining.
B. artificial intelligence
C. big data computing
D. internet of things
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