Hypothesis space and inductive bias. (4 points) We want to learn an unknown func
ID: 3711581 • Letter: H
Question
Hypothesis space and inductive bias. (4 points) We want to learn an unknown function f that takes n input arguments x1; x2; : : : ; xn and produces one output y. The input variables are boolean, i.e. each xi can be either T (true) or F (false). The output variable y can take on one of k different values. An example is a healthcare scenario where each of the xi corresponds to a symptom (the patient has the symptom or not), and y corresponds to the diagnosis (there are k diseases that can be diagnosed). (a) Let’s consider the hypothesis space H consisting of all functions that take n such 2- valued input arguments and produce one k-valued output. How many hypotheses are there in H? Briefly explain your answer. (b) Is the inductive bias in H high or low? What are the implications of this for a machine learning algorithm that tries to learn the unknown function f from training data? (c) Say that you get a training dataset with p different training examples, each of the form ((x1; x2; : : : ; xn); y). How many hypotheses in H are consistent with these training examples? Briefly explain your answer.
Explanation / Answer
Answer:
In Hypothesis space and Inductive bias has a alternate components with a functional variables f(x) & f(y). The following are the output value when the function f(x) i.e. Input value. The function f(y) i.e., output value i.e f(y). The following are similar value obtained.
1. If value f(x) = 0 then , f(x) >=n , Value output will be Hypotheis f(y) = h(k) = 1.
2. If value f(x) = 1 then, f(x)<=n , Value output will be hypotheis f(y) = h(k) = 0.
Where, h(k) = hypothesis regression value.
The hyposis scenario will be arranged variable in the output hypothesis space h(k) that will be considered. The value of the hypothesis will be Low = 0 & High = 1.
According to the machine learning algorithms functional values.
1. Candiate can be considered the target concept will be obtaied in the hypothesis space value H.
2. In the given regression value attributes input x and the output y can be linear, thus it will delete the errors.
The important concept about the machine learning algorithms will be considered according to the inductive bias value input.
Every input value of the Naive bayes algorthim indicates every input value depends on the output class and attributies.
Every value depends on the different inductive baises algorthims input value.
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