Independent and dependent variables for predictions What independent and dependent variables are supposed to be used in our models?
Does Y equal the leading variable of this:
Dependent variable= IF (close_price_tomorrow / open_price_tomorrow) >= 1.01 THEN 1 ELSE 0
Are the independent variables or Xs the following
(6) Dependent variable= IF (close_price_tomorrow / open_price_tomorrow) >= 1.01 THEN 1 ELSE 0
The recent assignment about manually calculating the Naive Bayes and KNN was very helpful.
However, they only have 2 independent variables (tenure, spend)
and one dependent variable (churn).
The Bayes with many independent variables is easier to understand than the KNN with more
than 2 independent variables.
It is difficult to think about calculating dissimilarity based on the Pythagorean theorem
in 6 dimensions as opposed to 2 dimensions.
What independent and dependent variables should we use for predictions?
How do you suggest changing our thinking of Naive Bayes and KNN
with 6 independent variables instead of only 2?
Answers and follow-up questions Answer or follow-up question 1
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