Data Science Asked on September 5, 2021
I am studying concept learning, and I am focusing on the concept of consistency for an hypotesis.
Consider an Hypotesis $h$, I have understood that it is consistent with a training set $D$ iff $h(x)=c(x)$ where $c(x)$ is the concept and this has to be verified for every sample $x$ in $D$.
For example consider the following training set:
and the following hypotesis:
$h_1=<?,?,?,Strong,?,?>$
I have that this is not consistent with D because for the example $3$ in $D$ we have $h(x)!=c(x)$.
I don’t understand why this hypotesis is not consistent.
Infact, consider the following hypotesis:
$h=<Sunny,Warm,?,Strong,?,?>$
this is consistent with $D$ because for each example in $D$ we have $h(x)=c(x)$.
But why the first hypotesis $h_1$ is not consistent while the second,$h$, is consistent?
Can somebody please explain this to me?
I'm not especially familiar with this but from the example provided we can deduce that:
EnjoySport
in the example) has the same value for any instance in the subset obtained by applying it.First case: $h_1=<?,?,?,Strong,?,?>$. All 4 instances in the data satisfy $h_1$, so the subset satisfying $h_1$ is the whole data. However the concept EnjoySport
can have two values for this subset, so $h_1$ is not consistent.
Second case: $h_2=<Sunny,Warm,?,Strong,?,?>$. This hypothesis is more precise than $h_1$: the subset of instances which satisfy $h_2$ is ${1,2,4}$. The concept EnjoySport
always have value Yes
for every instance in this subset, so $h_2$ is consistent with the data.
Intuitively, the idea is that an hypothesis is consistent with the data if knowing the values specified by the hypothesis gives a 100% certainty about the value of the target variable.
Correct answer by Erwan on September 5, 2021
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