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Weka Tutorial 37: Weighted Averages of Scores (Model Evaluation) Video Lecture | Weka Tutorial - Data & Analytics
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FAQs on Weka Tutorial 37: Weighted Averages of Scores (Model Evaluation) Video Lecture - Weka Tutorial - Data & Analytics
| 1. What is a weighted average of scores in model evaluation? |  |
Ans. A weighted average of scores in model evaluation is a technique used to combine multiple evaluation metrics by assigning different weights to each metric. It allows us to prioritize certain metrics over others based on their importance in the evaluation process.
| 2. How can weighted averages of scores improve model evaluation? | |
Ans. Weighted averages of scores can improve model evaluation by providing a more comprehensive and balanced view of the model's performance. By assigning appropriate weights to different evaluation metrics, we can reflect the relative importance of each metric and obtain a more accurate assessment of the model's overall effectiveness.
| 3. What factors should be considered when assigning weights to evaluation metrics? | |
Ans. When assigning weights to evaluation metrics, several factors should be considered. These factors include the domain knowledge and expertise of the evaluators, the specific objectives of the model, and the preferences of the stakeholders. It is important to carefully assess the relevance and significance of each metric in relation to the desired outcomes.
| 4. How can one determine the appropriate weights for evaluation metrics? | |
Ans. Determining the appropriate weights for evaluation metrics is often a subjective process that requires expert judgment and stakeholder input. One common approach is to conduct a survey or consultation with domain experts and stakeholders to gather their opinions on the relative importance of each metric. Alternatively, techniques such as Analytic Hierarchy Process (AHP) can be used to systematically derive the weights based on pairwise comparisons.
| 5. Are there any limitations or challenges associated with using weighted averages of scores in model evaluation? | |
Ans. Yes, there are some limitations and challenges associated with using weighted averages of scores in model evaluation. One challenge is the subjective nature of assigning weights, which can introduce bias if not carefully handled. Additionally, the choice of evaluation metrics and their corresponding weights may vary depending on the specific context and problem domain. It is essential to regularly review and update the weights to ensure they remain relevant and aligned with the evolving needs of the evaluation process.
Text Transcript from Video
hi this is going to be an interesting
week a tutorial in this tutorial I'm
going to discuss how Waker actually
calculates the weighted average of the
performance measures and I also got this
question from the audience that she gets
this binary classification problem with
+ + SN + y now in fuse if you carefully
take a look at the position and recall
she is getting for in class and white
class for in class she's getting this
positional recall and the if measure is
a straightforward harmonic mean of this
precision and recall for y class again
in a similar way she gets the precision
and recall and this F measure is
actually the harmonic mean of this
position recall
now Weka gives you an interesting
measure called weighted average which is
this row here now in this row you can
see that the user user is getting the
position point four seven eight and
recall point five zero two
but her F measure weighted average F
measure is point four three seven now
this is interesting because this point
four three seven here is not a harmonic
mean of the weighted average position
recall she gets point four three seven
is not the harmonic mean of 0.478 and
point five zero two then she asked this
question that how does then wake a
calculate the weighted average for a
given data set so I'm trying to discuss
this in this tutorial now if you take a
careful look then you can see that in
waken in order to calculate the weighted
average of these scores it first gets
for example we are just interested in in
the measure weighted average F measure
because that's giving us trouble to
calculate the weighted average if
measure winkin first takes the f measure
point two to three of at the end class
and then multiplies that point two to
three with the number of instances in
the data set that are labeled as M
in the second set Waker takes the
measure point six three three of the
white class and it multiplies that
number point six to three with the
number of instances in the day say that
labeled as Y now to get the weighted
average point four three seven here it
then
sums those two numbers up and divide
that number by the total number of
instances in in in your data set okay so
if you want to take a look at it point
in quantitative terms in more practical
way I just put these numbers in in a
spreadsheet you can see that this point
two to three is the F major for the
implants and 476 is the number of
instances in hard data set that are
labeled as and seventy one plus four or
five and sorry and this is this cell
actually is representing the
multiplication of the values at column
at cell a1 and cell b1 in a similar way
this is the F score point six three
three for the white class for the users
data set and this b2 cell is actually
the number of instances in hard data set
that are labeled as Y and this cell c2
is actually the multiplication of these
two values now 995 is the summation of
the instances labeled as n and labeled
as why she has total number of 903 s
total 995 instances in a dataset and
this cell actually is summing up these
two values here okay you can see that
it's c1 plus c2
now the weighted average if measure here
is actually the value at C 4 divided by
evaluate before that gives us point four
three six eight
in other words this can be represented
as point four three seven as we see from
the wakers output so this is an
interesting problem because at first
glance many may consider that this
weighted average measure is going to be
the harmonic mean of the weighted
average position and whether it was
recall but it's not actually the case
lekha calculates the weighted average
all the weighted average scores here TP
rated pr8 positional recall if measure
and roc area in the way I just have
discussed here I hope that helps I'll be
back with more tutorials till then have
a nice time if you are benefited from
these tutorials please click like and if
you want to get more updates whenever I
post a tutorial on the YouTube channel
if you want to get updates then please
subscribe thank you very much
week a tutorial in this tutorial I'm
going to discuss how Waker actually
calculates the weighted average of the
performance measures and I also got this
question from the audience that she gets
this binary classification problem with
+ + SN + y now in fuse if you carefully
take a look at the position and recall
she is getting for in class and white
class for in class she's getting this
positional recall and the if measure is
a straightforward harmonic mean of this
precision and recall for y class again
in a similar way she gets the precision
and recall and this F measure is
actually the harmonic mean of this
position recall
now Weka gives you an interesting
measure called weighted average which is
this row here now in this row you can
see that the user user is getting the
position point four seven eight and
recall point five zero two
but her F measure weighted average F
measure is point four three seven now
this is interesting because this point
four three seven here is not a harmonic
mean of the weighted average position
recall she gets point four three seven
is not the harmonic mean of 0.478 and
point five zero two then she asked this
question that how does then wake a
calculate the weighted average for a
given data set so I'm trying to discuss
this in this tutorial now if you take a
careful look then you can see that in
waken in order to calculate the weighted
average of these scores it first gets
for example we are just interested in in
the measure weighted average F measure
because that's giving us trouble to
calculate the weighted average if
measure winkin first takes the f measure
point two to three of at the end class
and then multiplies that point two to
three with the number of instances in
the data set that are labeled as M
in the second set Waker takes the
measure point six three three of the
white class and it multiplies that
number point six to three with the
number of instances in the day say that
labeled as Y now to get the weighted
average point four three seven here it
then
sums those two numbers up and divide
that number by the total number of
instances in in in your data set okay so
if you want to take a look at it point
in quantitative terms in more practical
way I just put these numbers in in a
spreadsheet you can see that this point
two to three is the F major for the
implants and 476 is the number of
instances in hard data set that are
labeled as and seventy one plus four or
five and sorry and this is this cell
actually is representing the
multiplication of the values at column
at cell a1 and cell b1 in a similar way
this is the F score point six three
three for the white class for the users
data set and this b2 cell is actually
the number of instances in hard data set
that are labeled as Y and this cell c2
is actually the multiplication of these
two values now 995 is the summation of
the instances labeled as n and labeled
as why she has total number of 903 s
total 995 instances in a dataset and
this cell actually is summing up these
two values here okay you can see that
it's c1 plus c2
now the weighted average if measure here
is actually the value at C 4 divided by
evaluate before that gives us point four
three six eight
in other words this can be represented
as point four three seven as we see from
the wakers output so this is an
interesting problem because at first
glance many may consider that this
weighted average measure is going to be
the harmonic mean of the weighted
average position and whether it was
recall but it's not actually the case
lekha calculates the weighted average
all the weighted average scores here TP
rated pr8 positional recall if measure
and roc area in the way I just have
discussed here I hope that helps I'll be
back with more tutorials till then have
a nice time if you are benefited from
these tutorials please click like and if
you want to get more updates whenever I
post a tutorial on the YouTube channel
if you want to get updates then please
subscribe thank you very much
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Nov 22, 2024 Last updated |
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