What does it mean that an hypotesis is consistent? 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:
$h_1=&lt;?,?,?,Strong,?,?&gt;$
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:
$h=&lt;Sunny,Warm,?,Strong,?,?&gt;$
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:

An hypothesis is a partial assignment of values to the features. That is, by &quot;applying the hypothesis&quot; we obtain a subset of instances for which the features satisfy the hypothesis.
An hypothesis is consistent with the data if the target variable (called &quot;concept&quot; apparently, here EnjoySport in the example) has the same value for any instance in the subset obtained by applying it.

First case: $h_1=&lt;?,?,?,Strong,?,?&gt;$. 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: $h_2=&lt;Sunny,Warm,?,Strong,?,?&gt;$. 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.