Different Values in Training and Testing set
On principle, you should try to achieve a fairly balanced distribution between your training and testing sets. Otherwise your model might not make appropriate generalizations.  Additionally, you should use some form of normalization such as feature scaling.

It is difficult (or even impossible) to understand your data distribution by simply looking at the range.  It would be better to look at the mean/median or even a histogram/normal curve.

Let's say, for example, that our ordered column A in your training/testing sets look as such (before normalization):

\begin{array}{|c|}
\hline
&amp;column \ A \ split\\
\hline
training &amp;&amp; testing \\
0 &amp;&amp; 0\\
4 &amp;&amp; 2\\
5 &amp;&amp; 3\\
7 &amp;&amp; 3\\
9 &amp;&amp; 8\\
15 &amp;&amp; 11\\
23 &amp;&amp; 15\\
30 &amp;&amp; 80\\
\hline
\end{array}

\begin{array}{|c|}
\hline
&amp;set\ metrics\\
\hline
&amp; training &amp; testing\\
range &amp; [0,30] &amp; [0,80]\\
mean &amp; 11.625 &amp; 15.250\\
median &amp; 8.0 &amp; 5.5\\
stddev &amp; 10.309 &amp; 26.650\\
\hline
\end{array}

\begin{align}
\large\frac{1}{\sqrt{2\pi}\cdot\sigma}e^{\frac{-(x-\mu)^2}{2\sigma^2}}
\end{align}


(red=training, blue=testing)

Here we can see that despite the large range discrepancy, our sets are relatively balanced.

We can observe a problematic training/testing split in this example where we shift the training mean from 11.625 to 100:



Moreover, we could generate another hypothetical dataset with a bad training/testing split where our set ranges are identical.



TL;DR:

Do not evaluate the set distribution by range; instead use mean/median or histogram/normal curve.  Also normalize your data.