How Can We Minimize Prediction Errors in Binary Variable Analysis?

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SUMMARY

This discussion focuses on minimizing prediction errors in binary variable analysis, specifically for the binary variable Y with equal probabilities P(Y = 1) = P(Y = 0) = 0.5. The random variable X is uniformly distributed on [0, 5] when Y = 0 and [4, 9] when Y = 1. The means of X for each case are calculated as 2.5 for Y = 0 and 7.5 for Y = 1. The conditional probability P(Y = 1|X = x) is defined for different ranges of X, revealing that exact prediction of Y from X is impossible when X is between 4 and 5.

PREREQUISITES
  • Understanding of binary variables and their distributions
  • Familiarity with conditional probability concepts
  • Knowledge of uniform distributions and their properties
  • Basic statistical concepts such as expected value (E[X])
NEXT STEPS
  • Study the concept of conditional probability in depth
  • Learn about the implications of uniform distributions in statistical analysis
  • Explore methods for minimizing prediction errors in binary classification
  • Investigate Bayesian inference techniques for binary variable prediction
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Statisticians, data scientists, and machine learning practitioners focused on binary classification and prediction error minimization will benefit from this discussion.

Luksdoc
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Homework Statement



Let Y be binary variable P(Y = 1) = P(Y = 0) = 0.5 and X a random variable uniform on [0,5] when Y = 0 and uniform [4, 9] when Y = 1. Draw mean of X and P(Y = 1|X = x) as functions of x. What is the minimum probability of rejection to predict Y from X without mistake.


Homework Equations





The Attempt at a Solution



Since Y acts like some "switch", I considered two independent distributions of X given Y: p = 1/5 on [0,5] (for Y = 0) and the other one p = 1/5 on [4,9] (for Y = 1). So two means are: 2.5 (for Y = 0) and 7.5 for (for Y = 1).

For P(Y = 1|X = x):
if X in [0, 4]: P(Y = 1|X = x) = 0
if X in [5, 9]: P(Y = 1|X = x) = 1
if X in [4, 5]: P(Y = 1|X = x) = 0.5

for this "What is the minimum probability of rejection to predict Y from X without mistake." I have no idea.
 
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Luksdoc said:

Homework Statement



Let Y be binary variable P(Y = 1) = P(Y = 0) = 0.5 and X a random variable uniform on [0,5] when Y = 0 and uniform [4, 9] when Y = 1. Draw mean of X and P(Y = 1|X = x) as functions of x. What is the minimum probability of rejection to predict Y from X without mistake.


Homework Equations





The Attempt at a Solution



Since Y acts like some "switch", I considered two independe[/color]nt distributions of X given Y: p = 1/5 on [0,5] (for Y = 0) and the other one p = 1/5 on [4,9] (for Y = 1). So two means are: 2.5 (for Y = 0) and 7.5 for (for Y = 1).
I think the problem is asking you for E[X] as a function of x, similar to what you did for P(Y=1|X=x). If x is in [0,4], what is E[X]? And so on.
 
Luksdoc said:

Homework Statement



Let Y be binary variable P(Y = 1) = P(Y = 0) = 0.5 and X a random variable uniform on [0,5] when Y = 0 and uniform [4, 9] when Y = 1. Draw mean of X and P(Y = 1|X = x) as functions of x. What is the minimum probability of rejection to predict Y from X without mistake.


Homework Equations





The Attempt at a Solution



Since Y acts like some "switch", I considered two independent distributions of X given Y: p = 1/5 on [0,5] (for Y = 0) and the other one p = 1/5 on [4,9] (for Y = 1). So two means are: 2.5 (for Y = 0) and 7.5 for (for Y = 1).

For P(Y = 1|X = x):
if X in [0, 4]: P(Y = 1|X = x) = 0
if X in [5, 9]: P(Y = 1|X = x) = 1
if X in [4, 5]: P(Y = 1|X = x) = 0.5

for this "What is the minimum probability of rejection to predict Y from X without mistake." I have no idea.

I don't see any way of predicting Y exactly from X in all cases. If we happen to observe a value of X between 4 and 5, Y is allowed to be 0 or 1, and there is no way to be sure which is correct.

RGV
 

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