A. The F1 score is defined as a measure of a model’s performance.
Q. How is F1 score is used in Data Science?
A. The average of Precision and Recall of a model is nothing but F1 score measure. Based on the results, the F1 score is 1 then it is classified as best and 0 being the worst.
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Q. What is ODS(Operational Data Source) in Data Science ?
A. An operational data store (“ODS”) is a database designed to integrate data from multiple sources for additional operations on the data. Unlike a master data store, the data is not sent back to operational systems. It may be passed for further operations and to the data warehouse for reporting.
Q. What is the difference between Machine learning Vs Data Mining Data Science?
A. Data Mining is about working on unlimited data and then extract it to a level anywhere the unusual and unknown patterns are identified. Machine Learning is any method about a study whether it closely relates to design, development concerning the algorithms that provide an ability to certain computers to capacity to learn.
Q. What are confounding variables?
A. These are obvious variables in a scientific model that correlates directly or inversely with both the subject and the objective variable. The study fails to account for the confounding factor.
Q. How to get indices of N maximum values in a NumPy array in data science?
A. We can get the indices of N maximum values in a NumPy array using the below Example code:
import numpy as np
arr = np.array([1, 3, 2, 4, 5])
print(arr.argsort()[-3:][::-1])
Output: [ 4 3 1 ].
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