Data & Analytics Exam > Data & Analytics Videos > Weka Tutorial > Weka Tutorial 28: ROC Curves and AUC (Model Evaluation)
Weka Tutorial 28: ROC Curves and AUC (Model Evaluation) Video Lecture | Weka Tutorial - Data & Analytics
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FAQs on Weka Tutorial 28: ROC Curves and AUC (Model Evaluation) Video Lecture - Weka Tutorial - Data & Analytics
| 1. What is a ROC curve and how is it used in model evaluation? |  |
Ans. A ROC (Receiver Operating Characteristic) curve is a graphical representation of the performance of a binary classification model. It is created by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. ROC curves are used to assess the trade-off between the model's true positive rate and false positive rate, allowing us to choose an optimal threshold for classification.
| 2. What is AUC and why is it important in model evaluation? | |
Ans. AUC (Area Under the Curve) is a metric used to measure the overall performance of a binary classification model. It represents the probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative instance. AUC ranges between 0 and 1, where a higher value indicates a better model. AUC is important in model evaluation as it provides a single number that summarizes the model's discriminatory power, making it easier to compare different models.
| 3. How can ROC curves and AUC be used to compare multiple models? | |
Ans. ROC curves and AUC can be used to compare multiple models by plotting their respective ROC curves on the same graph and comparing their AUC values. The model with a higher AUC value generally performs better in terms of classification accuracy. By visually comparing the ROC curves, one can also identify the model that achieves a better trade-off between sensitivity and specificity at different threshold settings.
| 4. Can ROC curves and AUC be used for multiclass classification problems? | |
Ans. ROC curves and AUC are primarily designed for binary classification problems where there are only two classes. However, they can be extended to evaluate multiclass classification models by using one-vs-all or one-vs-one approaches. In the one-vs-all approach, each class is treated as positive while the remaining classes are treated as negative, resulting in multiple ROC curves and AUC values. In the one-vs-one approach, a separate ROC curve and AUC value is calculated for each pair of classes.
| 5. Are ROC curves and AUC affected by imbalanced datasets? | |
Ans. Yes, ROC curves and AUC can be affected by imbalanced datasets where one class is significantly underrepresented compared to the other. In such cases, the ROC curve may not accurately reflect the model's performance, especially for the minority class. A high AUC value can be misleading if it is primarily driven by the majority class. To mitigate this, other evaluation metrics like precision, recall, and F1-score should also be considered, along with the ROC curve and AUC, when dealing with imbalanced datasets.
Text Transcript from Video
oh and welcome to another wake a
tutorial in this tutorial I'm going to
show you how you can use wake add to
produce the receiver operating
characteristic or ROC curve for your
experiment so actually this tutorial
will have another part / - where I'll be
where I'll try to show you how you can
compare multiple number of roc curves
for multiple number of classifiers to
see those to compare the results but in
this tutorial I'm just going to show you
how you can produce one single ROC curve
for one classifier so in order to do
that I'm going to the Explorer
application of Weka and I'm opening the
file diabetes dot our file for the first
time so I'm going to the wake-up
repository here and inside data set data
I have the diabetes start file so you
can see that if I go to the class of
this our file we are having two classes
tested negative and tested positive that
means whether you are having a diabetes
or not so in order to produce a ROC
curve for any classifier the first task
is to choose a classifier and to
classify the instances of this our file
so we're going to click on start and you
can see that tape measure is 82.2% for
the negative class sixty four point
three percent for the positive class and
76 percent it has a combination of those
and if you if you just take a look at
the evaluation measures used in data
mining frequently precision recall
length measure and accuracy these are
not considered as cost and cost
sensitive analysis measures so what is
the cost sensitive analyzes measure so
let me just briefly tell you that if
your data set is heavily skewed for
example 90 percent of its items or
instances are positive instances
and 10% of the instances are negative
instances so in that case if you produce
any classifier that's really really bad
that doesn't know the difference between
the positive and negative instances it
just blindly tells everything as
positive so in that case it's a black
bad classifier but it is at least having
90% of the accuracy because it tells
everything as positive and your data set
is having 90 percent of the 90 percent
items as labeled as positive so its
accuracy is as high as 90 percent but
that's not the true or the appropriate
accuracy for the classifier because it's
really really badly designed so these
measures they have measure precision
recall and accuracy is here but you can
all kind accurate is here that yours is
seventy six point three percent so maybe
they are not reflecting the appropriate
accuracy they are not reflecting the
appropriate evolution of your classifier
so rock car or receiver operating
characteristic curve is a visualization
tool by which you can tell easily easily
that in a clinical sensitive manner of
course that whether your classifier is
really appropriate what you appropriate
one or not so in order to know ROC
curves more you have to dig in for more
so we have these cost insensitive
analyzes here precision reconnect
measure now we're going to produce a ROC
curve so on the results list right click
on on the result here and you can see
that there there is an option called
visualize threshold carve and if I just
hover my mouse over there you can see
that we are having two options one is
tested negative and tested positive so
this means that you if you are trying to
produce a rock car for a to plus problem
then you are having options to have ROC
curves for the two classes if you can
recall we are having tested negative and
tested positive classes for diabetes our
file so we can produce two types
of rock carts there one for negative
classes and other for the positive
classes so for example we're just
interested in tests and tested negative
instances so we click on that and you
can see that instantly the ROC curve has
been produced so in ROC curve you are
having false positive rates on the
x-axis and the true positive rate also
called the recall on the y-axis and this
has been just drawn so in ROC curve the
main goal is to have this car lot more
to the upper left corner which is one so
this means that you are having very very
good classifier your classifiers ROC
curve is 10 is 10 is more to this
direction okay in in a different way to
interpret this in a different way you
can also find area under rock or a under
ROC curve or a UC which is clearly given
here that is ad 1.8 6 percent and 80 1.8
6% is a very very reasonable amount of
AUC so you can see also see that this
ROC curve is is not it is not is
reasonable one up to me it's a good one
it's not a bad one so we are having a
very very reasonable ROC curve here and
also the number tells the truth at
eighty one eight point eight six percent
of the AUC or in the area under the ROC
curve is also a very reasonable AUC so
in this way you can you can really
produce the ROC curve for your
experiment I hope that helps I'll be
back to give you more but the next
tutorial is going to be how to get the
position recall curve for your
experiment and then I'll tell you how
you can compare multiple numbers
rock cars thank you
tutorial in this tutorial I'm going to
show you how you can use wake add to
produce the receiver operating
characteristic or ROC curve for your
experiment so actually this tutorial
will have another part / - where I'll be
where I'll try to show you how you can
compare multiple number of roc curves
for multiple number of classifiers to
see those to compare the results but in
this tutorial I'm just going to show you
how you can produce one single ROC curve
for one classifier so in order to do
that I'm going to the Explorer
application of Weka and I'm opening the
file diabetes dot our file for the first
time so I'm going to the wake-up
repository here and inside data set data
I have the diabetes start file so you
can see that if I go to the class of
this our file we are having two classes
tested negative and tested positive that
means whether you are having a diabetes
or not so in order to produce a ROC
curve for any classifier the first task
is to choose a classifier and to
classify the instances of this our file
so we're going to click on start and you
can see that tape measure is 82.2% for
the negative class sixty four point
three percent for the positive class and
76 percent it has a combination of those
and if you if you just take a look at
the evaluation measures used in data
mining frequently precision recall
length measure and accuracy these are
not considered as cost and cost
sensitive analysis measures so what is
the cost sensitive analyzes measure so
let me just briefly tell you that if
your data set is heavily skewed for
example 90 percent of its items or
instances are positive instances
and 10% of the instances are negative
instances so in that case if you produce
any classifier that's really really bad
that doesn't know the difference between
the positive and negative instances it
just blindly tells everything as
positive so in that case it's a black
bad classifier but it is at least having
90% of the accuracy because it tells
everything as positive and your data set
is having 90 percent of the 90 percent
items as labeled as positive so its
accuracy is as high as 90 percent but
that's not the true or the appropriate
accuracy for the classifier because it's
really really badly designed so these
measures they have measure precision
recall and accuracy is here but you can
all kind accurate is here that yours is
seventy six point three percent so maybe
they are not reflecting the appropriate
accuracy they are not reflecting the
appropriate evolution of your classifier
so rock car or receiver operating
characteristic curve is a visualization
tool by which you can tell easily easily
that in a clinical sensitive manner of
course that whether your classifier is
really appropriate what you appropriate
one or not so in order to know ROC
curves more you have to dig in for more
so we have these cost insensitive
analyzes here precision reconnect
measure now we're going to produce a ROC
curve so on the results list right click
on on the result here and you can see
that there there is an option called
visualize threshold carve and if I just
hover my mouse over there you can see
that we are having two options one is
tested negative and tested positive so
this means that you if you are trying to
produce a rock car for a to plus problem
then you are having options to have ROC
curves for the two classes if you can
recall we are having tested negative and
tested positive classes for diabetes our
file so we can produce two types
of rock carts there one for negative
classes and other for the positive
classes so for example we're just
interested in tests and tested negative
instances so we click on that and you
can see that instantly the ROC curve has
been produced so in ROC curve you are
having false positive rates on the
x-axis and the true positive rate also
called the recall on the y-axis and this
has been just drawn so in ROC curve the
main goal is to have this car lot more
to the upper left corner which is one so
this means that you are having very very
good classifier your classifiers ROC
curve is 10 is 10 is more to this
direction okay in in a different way to
interpret this in a different way you
can also find area under rock or a under
ROC curve or a UC which is clearly given
here that is ad 1.8 6 percent and 80 1.8
6% is a very very reasonable amount of
AUC so you can see also see that this
ROC curve is is not it is not is
reasonable one up to me it's a good one
it's not a bad one so we are having a
very very reasonable ROC curve here and
also the number tells the truth at
eighty one eight point eight six percent
of the AUC or in the area under the ROC
curve is also a very reasonable AUC so
in this way you can you can really
produce the ROC curve for your
experiment I hope that helps I'll be
back to give you more but the next
tutorial is going to be how to get the
position recall curve for your
experiment and then I'll tell you how
you can compare multiple numbers
rock cars thank you
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Dec 23, 2024 Last updated |
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