Tutorial 34- Performance Metrics For Classification Problem In Machine Learning- Part1

Summary

In this educational video, the host Krishna introduces key metrics for evaluating classification problems in machine learning. The script covers confusion matrices, accuracy, type 1 and 2 errors, recall, precision, and the F-beta score. It emphasizes the importance of selecting the right metrics for model evaluation, especially in the context of balanced and imbalanced datasets. The video promises a continuation with ROC curves, AUC scores, and PR curves, followed by practical implementation on an imbalanced dataset to demonstrate the impact of different metrics on model accuracy.

Takeaways

• 📈 The video discusses various metrics for evaluating classification problems in machine learning, emphasizing the importance of selecting the right metrics for model evaluation.
• 📊 The script introduces the concept of a confusion matrix, explaining its components like true positives, false positives, true negatives, and false negatives, which are crucial for understanding model performance.
• 🔢 The video explains accuracy as a metric for balanced datasets, where the model's performance is measured by the proportion of correct predictions over the total predictions.
• ⚖️ It highlights the difference between balanced and imbalanced datasets, noting that accuracy may not be a reliable metric for the latter due to potential bias towards the majority class.
• 👀 The importance of recall (true positive rate) and precision (positive prediction value) is emphasized for evaluating models on imbalanced datasets, as they focus on reducing type 1 and type 2 errors respectively.
• 🤖 The script introduces the F-beta score, a metric that combines recall and precision, allowing for the weighting of the importance of false positives versus false negatives through the beta parameter.
• 📚 The video outlines the use of precision in scenarios where false positives are particularly costly, such as in spam detection, while recall is prioritized in cases like medical diagnosis to avoid missing critical conditions.
• 🧩 The F-beta score is detailed with its formula and application, showing how it can be adjusted with the beta value to reflect the relative importance of precision and recall in different contexts.
• 🔍 The video promises to cover additional metrics like Cohen's Kappa, ROC curve, AUC score, and PR curve in the next part of the series, indicating a comprehensive approach to model evaluation.
• 🛠️ Part three of the video series will include a practical implementation on an imbalanced dataset, demonstrating how to apply these metrics and understand their implications in real-world scenarios.
• 🔄 The script concludes by stressing the need for continuous learning and revision of these concepts to ensure a thorough understanding of machine learning model evaluation.

Q & A

What is the main topic of the video?

The main topic of the video is discussing various metrics used in classification problems for evaluating machine learning algorithms.

What are the two types of classification problem statements mentioned in the video?

The two types of classification problem statements are those based on class labels and those based on probabilities.

What is a confusion matrix?

A confusion matrix is a 2x2 table used in binary classification problems to evaluate the performance of a classification model. It includes true positives, true negatives, false positives, and false negatives.

What is the default threshold value used in binary classification problems?

The default threshold value used in binary classification problems is 0.5.

What is the importance of selecting the right metrics for evaluating a machine learning model?

Selecting the right metrics is crucial for understanding how well a machine learning model is predicting. Incorrect metrics can lead to a false sense of accuracy, which may result in poor model performance when deployed in production.

What is the difference between a balanced and an imbalanced dataset in the context of classification problems?

A balanced dataset has approximately the same number of instances for each class, while an imbalanced dataset has a significantly higher number of instances for one class compared to others.

Why is accuracy not a reliable metric for imbalanced datasets?

Accuracy can be misleading for imbalanced datasets because a model could predict the majority class for all instances and still achieve high accuracy, even if it fails to correctly classify the minority class.

What are recall and precision, and when should they be used?

Recall (or true positive rate) measures the proportion of actual positives correctly predicted by the model. Precision (or positive prediction value) measures the proportion of predicted positives that are actual positives. They should be used when dealing with imbalanced datasets to focus on reducing false negatives or false positives, respectively.

What is the F beta score and how is it related to the F1 score?

The F beta score is a measure that combines precision and recall, with a parameter beta that allows for weighting the importance of recall over precision (or vice versa). When beta is equal to 1, the F beta score is known as the F1 score, which gives equal importance to both recall and precision.

What are the additional metrics that will be discussed in the next part of the video?

In the next part of the video, the presenter plans to discuss Cohen's Kappa, ROC curve, AUC score, and PR curve, along with two more metrics.

How will the presenter demonstrate the application of these metrics in the third part of the video?

In the third part of the video, the presenter will implement a practical problem statement using an imbalanced dataset and apply the discussed metrics to show how they affect the evaluation of a machine learning model.

Outlines

00:00

📊 Introduction to Classification Metrics

The speaker, Krishna, introduces the video series focusing on classification metrics in machine learning. He outlines the importance of understanding various metrics such as the confusion matrix, accuracy, type 1 and type 2 errors, recall, precision, and F-beta score. The video is structured in parts, with part one covering the basics of classification metrics, part two diving into more complex metrics like Cohen's Kappa, ROC curve, AUC score, and PR curve, and part three applying these metrics to an imbalanced dataset. Krishna emphasizes the significance of choosing the right metrics to evaluate a machine learning model's performance, especially before deploying it in a production environment.

05:01

🔍 Understanding Balanced and Imbalanced Datasets

This paragraph delves into the concepts of balanced and imbalanced datasets in binary classification problems. A balanced dataset is characterized by a similar number of positive and negative outcomes, whereas an imbalanced dataset has a disproportionate ratio of one class to the other. Krishna explains that accuracy is a suitable metric for balanced datasets, but for imbalanced ones, other metrics like recall, precision, and F-beta score are more appropriate. He also introduces the confusion matrix, a 2x2 grid that categorizes predictions into true positives, false positives, true negatives, and false negatives, which is essential for understanding model performance.

10:02

📈 The Importance of Recall and Precision

Krishna discusses the significance of recall and precision in the context of imbalanced datasets. Recall, also known as the true positive rate or sensitivity, measures the proportion of actual positives correctly predicted by the model. Precision, or positive predictive value, indicates the proportion of predicted positives that are true positives. The speaker highlights the importance of reducing both type 1 and type 2 errors and explains how to compute accuracy using the confusion matrix. He also provides examples to illustrate when to prioritize recall or precision based on the problem statement.

15:04

📉 The Role of F-Beta Score in Machine Learning

The paragraph introduces the F-beta score, a metric that combines both recall and precision to provide a balanced measure of model performance, especially in cases where both false positives and false negatives carry significant weight. Krishna explains the formula for calculating the F-beta score and how the choice of the beta value affects the emphasis on precision or recall. He provides guidance on selecting the beta value based on the relative importance of false positives and false negatives in a given problem statement and mentions that the F-beta score can range from F0.5 to F2, with F1 being the harmonic mean of precision and recall.

20:07

🔚 Conclusion and Future Content

In the concluding paragraph, Krishna summarizes the key points covered in the video and previews the content for the upcoming parts of the series. He stresses the importance of understanding and applying the correct metrics for evaluating machine learning models. The speaker encourages viewers to subscribe to the channel and revisit the video for a deeper understanding of the concepts discussed. The next part of the series will cover advanced metrics, and part three will demonstrate the application of these metrics in a practical scenario involving an imbalanced dataset.

Keywords

💡Confusion Matrix

A confusion matrix is a table used to evaluate the performance of a classification model. In the video, it is described as a 2x2 matrix for binary classification, where the rows represent the actual classes and the columns represent the predicted classes. It helps in visualizing the performance of an algorithm by showing true positives, true negatives, false positives, and false negatives. The script mentions it as the basis for understanding other metrics like accuracy, precision, recall, and F-beta score.

💡Accuracy

Accuracy is a performance measurement for classification problems that represents the ratio of correct predictions to the total number of predictions. The video script explains that accuracy is calculated by dividing the sum of true positives and true negatives by the total number of instances. It is noted that accuracy is a good metric for balanced datasets but can be misleading in imbalanced datasets.

💡Type 1 Error

Type 1 Error, also known as a false positive, occurs when a model incorrectly predicts a positive class when the actual class is negative. The video script emphasizes the importance of reducing Type 1 errors and explains that they can be quantified using the false positive rate, which is the proportion of actual negatives that are incorrectly labeled as positives.

💡Type 2 Error

Type 2 Error, also known as a false negative, is when a model incorrectly predicts a negative class when the actual class is positive. The script mentions Type 2 Error in the context of missing out on important positive instances, such as in health care scenarios where not identifying a disease can have serious consequences.

💡Recall

Recall, also referred to as the true positive rate or sensitivity, measures the ability of a classification model to find all the positive instances. The video script explains that high recall is important when the cost of false negatives is high, as in medical diagnosis, where missing a disease can be critical.

💡Precision

Precision, also known as positive predictive value, is the ratio of true positive predictions to the total number of positive predictions made by the model. The script uses the example of spam detection to illustrate the importance of precision, where a low false positive rate ensures that non-spam emails are not mistakenly marked as spam.

💡F-beta Score

The F-beta score is a weighted harmonic mean of precision and recall, where the 'beta' parameter allows for adjusting the relative importance of precision and recall. The video script explains that an F1 score, which is an F-beta score with beta equal to 1, balances the two metrics equally and is used when both false positives and false negatives are equally important.

💡ROC Curve

ROC stands for Receiver Operating Characteristic. The ROC curve is a graphical representation of a classifier's performance, plotting the true positive rate (recall) against the false positive rate (Type 1 Error) at various threshold settings. The script mentions that ROC curves will be discussed in part 2 of the video series, indicating its importance in evaluating classification models.

💡AUC Score

AUC, or Area Under the ROC Curve, is a single scalar value that summarizes the performance of a classification model. AUC measures the entire two-dimensional area underneath the entire ROC curve from 0 to 1. The script indicates that AUC score will be discussed in part 2, suggesting its relevance in understanding the overall effectiveness of a model.

💡Imbalanced Dataset

An imbalanced dataset is one where the number of instances in the different classes are not equal, leading to a higher bias towards the majority class. The video script discusses the challenges of using accuracy in imbalanced datasets and instead suggests using metrics like recall, precision, and F-beta score to better evaluate model performance.

💡Threshold Value

In classification problems, especially in binary classification, the threshold value determines the cutoff point for classifying instances into positive or negative classes. The script explains that the default threshold is often 0.5, but it can be adjusted based on the problem context, such as in healthcare diagnostics where a lower threshold might be chosen to avoid missing positive cases.

Highlights

Introduction to various metrics used in classification problems for evaluating machine learning algorithms.

Explanation of the confusion matrix, a fundamental tool for classification problem analysis.

Discussion on accuracy as a metric for balanced datasets in classification problems.

Introduction to Type 1 and Type 2 errors, crucial for understanding model performance.

Clarification of recall, also known as true positive rate, and its importance in model evaluation.

Precision, or positive predictive value, explained as a key metric for classification accuracy.

Introduction to F-beta score, a metric combining recall and precision for a balanced view of model performance.

The significance of choosing the right metrics to avoid wasting time and resources on ineffective models.

The impact of using incorrect metrics on model performance, especially in a production environment.

Explanation of class labels and probabilities in binary classification and their implications on model output.

The role of threshold values in determining class labels from probabilities in machine learning models.

Differentiation between balanced and imbalanced datasets and their effects on model bias.

The inappropriateness of using accuracy for evaluating models on imbalanced datasets.

How to compute accuracy for balanced datasets and its limitations.

The importance of reducing Type 1 and Type 2 errors to improve model performance.

Practical implementation of metrics on an imbalanced dataset in a future video part.

Upcoming discussion on advanced metrics like Cohen's Kappa, ROC curve, AUC score, and PR curve in part two of the video series.

The necessity to balance false positives and false negatives based on the problem statement's context.

The selection of beta values in F-beta score based on the relative importance of false positives and false negatives.

Final thoughts on the importance of understanding and applying the correct metrics for effective machine learning model evaluation.

Transcripts

hello my name is Krishna and welcome to

my youtube channel now this was one of

the most requested video by you all guys

so in this video we'll be discussing

about all the metrics in a

classification problem statement guys

this is just the part one and I have

listened down all the important metrics

that you can actually use for

understanding whether your machine

learning algorithm is predicting well or

not

okay so some of the metrics of a

confusion matrix okay

then we'll understand about accuracy

then we'll understand about type 1 error

type 2 error

then we have concepts like recall which

is also called as true positive red then

we will discuss about precision which is

also called as positive prediction value

okay

then we'll understand about F beta and

in the next part in the next video we'll

basically be understanding about Kohan

Kappa ROC curve AUC score and something

called as PR curve there are two more

metrics again which will I'll discuss in

the part two because I do not have space

to write it down so we'll just be

discussing about that in our next part -

in the part three I'll try to implement

a problem statement considering an

imbalance data set and I'll try to apply

all this particular matrix and I'll show

you that how the accuracy will look like

so make sure guys you watch this

particular video completely and make

sure that you understand things okay the

reason why I am saying even though you

are a very very good data scientist and

you know how to actually use a machine

learning algorithm with respect to your

data but if you are not using the

correct kind of metrics to find out how

good your model is then it is completely

a waste of time you know because if you

are not selected the right metrics and

then you are deployed your model to the

production right you'll be able to see

that because of the metrics because of

the wrong metrics that you have chosen

you have chosen that will actually give

you a very very bad accuracy again when

the model is actually deployed in the

production so let us go ahead and try to

understand the metrics and in this

particular video I will be making like a

story ok so part 1 will just be like a

story and then I will continue and I

will explain you each and every matrix

Omega now understand one thing that

suppose we have a problem statement so

this is a problem statement specifically

a classification problem statement ok

classification problem statement now in

classification problem statement right

there are two ways how you can solve the

classification problem one way is

basically through class labels suppose

you want to predict class labels ok the

next way is through probabilities

probabilities now suppose I if you let

me just consider a binary classification

in a binary classification I know there

will be two different classes A or B

suppose this is my a or B so my output

will either be a or B ok

by default the threshold value will be

taken as 0.5 what does this basically

mean suppose I am predicting with some

of my machine learning model like

logistic regression by default if I

predict if it is greater than 0.5 then

it would become a B class if it is less

than 0.5 then it becomes a a class I

mean less than or equal to 0.5 then it

would become a a class but in case of

probabilities here we have to also find

out the class labels how we have to

basically select the right threshold

value which is this p value okay and

lots a p value but i instead say some

threshold value in some of the health

care sector this threshold value may

decrease you will be saying that suppose

if a person is having cancer or not at

that time this threshold value should be

chosen in a proper way if it is not

chosen in the proper way

the person who is having cancer will be

missed out right so in probabilities we

will be discussing and in probabilities

what we have we have basically ROC curve

au seeker as a score at PR curve which

we'll be discussing in the part 2 in the

part 1 we'll be focusing more on this

class labels where our default

probability is 0.5 okay so I hope you

are getting it right so understand that

thing if we have a classification

problem usually what we do is that they

have two types of problem statements

over here with respect to the class

labels we need to find out what what is

the output of that particular record or

based on probabilities where we have to

first of all find out a threshold value

okay in logistic regulation I may find

out

the threshold value maybe 0.3 maybe 0.4

that basically means that if my output

is less than or equal to 0.4 it becomes

Class A if my output is greater than 0.4

then it becomes Class B right and this I

will be showing you how you can actually

find out with the help of ROC curve and

P arca okay so that will come in the

part two now let us go ahead one more

thing now over here now with respect to

this problem statement we have two

problem statement based on the output

okay based on the output suppose in in

this particular problem statement I have

thousand records okay

I have thousand records okay now with

respect to thousand records suppose this

is the binary classification problem

that basically means suppose I have 500

yes that basically 500 records which has

a yes as an output and I have 500 no as

my other output or I may have 600 years

or 400 no right now in this case what I

can suggest is that this looks like an

in this looks like a balanced data set

okay balanced data set basically means

that yes you have almost same number of

years and same number of no so both then

the output labels are almost same

similarly if you could take seven

hundred years and three hundred no this

is also fine this looks like a balanced

data set

okay now understand one thing guys why

why I am saying balanced data set over

here the number of years and no are

almost equal in this case you may be

suggesting Chris there is a difference

of four hundred but it is fine why I'll

tell you if we have this kind of data

set also and if we try to provide this

kind of data points to a machine

learning algorithm my machine learning

algorithm will not get biased based on

the maximum number of output okay but if

we have scenarios where in our data set

ranges and this is basically 70 to 30

right 70 to 30 percent basically 70 to

30 ratio here you have basically like a

60/40 ratio here you have 50/50 ratio

right but now if I go one more level

down like 8020 ratio 8020 ratio

basically means that suppose I have over

here 800 record and here I have

200 right now in this case when I

provide this kind of imbalance dataset

to my machine learning algorithm some of

the machine learning algorithm will get

biased based on the maximum number of

output okay now if we have a balanced

data set if we have a balanced data set

the type of metrics that is basically

used is something called as accuracy if

we have an imbalance data set at that

time we do not consider accuracy instead

we consider something called as recall

precision and F beta I'll explain you

about what exactly is a beta score which

is also called as the f1 score if you

have heard of most of it but this f1

score is derived by this beta value that

will it be discussing about now this

consider guys initially let us take that

suppose my data set is balanced okay at

that time I'll try to explain your

accuracy and then we will then

understand if our data becomes

imbalanced how do we solve this

particular problem okay so let us go

ahead I am just going to rub this thing

and if you have not understood just you

know just go back again see this

particular explanation what I've given

okay now first of all if we have a

binary classification problem guys we

need to understand what exactly is the

confusion matrix now understand one

thing guys concision matrix is nothing

but it is a 2 cross 2 matrix in case of

binary classification problem where the

top values are actually the actual

values okay the top values are the

actual values over here actual values

like 0 or 1 1 or 0 suppose I have to

consider this as 1 and 0 and similarly

in the left hand side this all mehar my

predicted values so this basically

values indicates that what my model has

actually predicted so this will also

become 1 and 0 so usually what we do is

that each and every field we specify

with some notations so the first field

in this case is something called as true

positive the second field is something

called as true false positive and the

third field is something called as false

negative and the fourth field is

something called as true negative we'll

try to understand more in depth what

exactly this of these fields mean okay

what is false positive what is false

negative and by default if I consider

this type 1 this is called as

type one error okay

so if I want to consider this false

positive this is basically called as a

type 1 error we we can also compute the

type 1 error with the help of false

positive rate okay false positive rate I

will define the formula in just a while

this FN is basically called as a type 2

error and this is also mentioned like

false negative rate now what is what do

what does this false positive mean now

if you want to define false positive

error how do we do is that we basically

consider this false values with respect

to your actual and predicted and the

false positive rate is basically given

by FB / FP plus TN okay this true

negative and this and always remember

guys these are your most accurate

results okay TP and DL and always your

aim should be okay in any classification

problem to reduce your type 1 error and

to reduce your type 2 error okay you

always have to focus on reducing your

type 1 error and reducing your type 2

error okay now understand one thing guys

since this is a balanced problem

statement right so what we do is that we

directly compute the accuracy now how

does we how do we compute the accuracy

is that we simply add T P plus TN PP +

TN / TP plus FP + SN + TN that basically

means I am just saying that this

diagonal elements which will actually be

giving me the right result divided by

total number of residents and this will

give us the accuracy why I am following

this particular stuff because I'm

considering this problem statement is my

balance data set okay

and during the balance data set usually

my model does not get biased based on

the different types of categories that

we have in this binary classification

problem okay but now what is what if my

data set is not balanced what if my data

set is not balanced let me give you a

very good example suppose one category 1

category I have some one hundred nine

hundred values suppose out of the

thousand records

I have nine hundred one category and one

hundred as my another category okay 100

as my another category now if I apply

just understand over here guys if I just

say that my model will bluntly say that

everything belongs to category A and not

to category B okay not to category B but

instead it will say that every I mean

every values or every touch data set

belongs to my category a instead of

category B okay so out of this I am just

suggesting that okay 900 values and I am

considering this is my touch data

suppose days okay suppose I am having

this test data or just consider that I

had my train data in my train did I had

1500 records out of which 1200 was my a

Class C category and 300 was my Class B

category and now I have divided that

into train - split my in my inside my

test data suppose I had around I'm just

considering an example in my test data I

had Class A as 900 records and Class B

has 100 records now suppose if my model

predicted all the classes over Class A

now in that specific place if I said TP

plus TN I'm just going to get the 90%

accuracy right because they're true

positive I will be getting 900 true

negative I will be getting 0 and if I do

the summation of all of this this will

be some thousand and this is in short

90% dozen of my accuracy right so this

is a problem right if we have an

imbalanced data set we cannot just use

accuracy because it will give you a very

very bad meaning about that particular

model you know you're just blindly

saying that it belongs to just one

category so if you have an imbalanced

data set you basically go with something

called as recall precision right and

something called as FB toughs code now

let us go ahead and try to understand

about recall and precision okay guys now

let us go ahead and just understand what

exactly is recall and precision

now guys here is my confusion matrix

here you have through positive false

positive false negative and true

negative I understand why do we use this

for an imbalanced data set now

understand one thing guys any kind of

data set that you have you should always

try to reduce your type

one error and type 2 error okay you

should always try to reduce this now

specifically when your data set is

imbalanced we should either focus on

recall and precision now what does

recall over here formula says recall

basically says that TP TP so these are

my actual values right these are my

actual these are my predicted values so

this basically says that TP / TP plus FN

okay TP / TP plus FN now what does this

says that out of the total positive

actual values how many values did be

correctly predicted positively okay this

is what this recall basically says again

I am repeating it guys out of the total

actual positive values one is positive

right I can say true or positive

anything out of all this how many

positive did we predict correctly that

is what this recall basically says

recall is also given by something called

as true positive rate it is also

mentioned by true positive rate or it is

also mentioned by sensitivity okay it is

also mentioned by sensitivity now

similarly if I consider about precision

it basically says TP / TP + SB okay now

what does this basically say out of the

total predicted positive result how many

results are actual positives okay here

we are actually focusing on the false

positives here in the record we were

actually focusing on false negatives

right so again I am repeating it what

does precision basically say out of the

total actual positive predicted results

how many were actually positive and what

is the prop proportion that were actual

positive that is what this particularly

precision basically say and for

precision we also specify another name

which is called as positive prediction

value suppose in your interview they

asked you what is actually positive

prediction value you have to explain the

same thing understand this thing guys

now when should we use a recall and

precision understand guys let me just

give you some very good example of

recall and precision so I have a use

case which is called as spam detection

in spam detection I have to focus on

precision why consider that suppose in

this false positive which basically says

that suppose this this mail is not a

spam okay suppose it is mail is not a

spam but the model has predicted that it

is a spam that is your false positive

okay this again I'm telling you guys the

mail is not a spam but it has been

predicted that it is a spam so this is

what it is saying zero basically means

not a spammer

but it has predicted that it is a spam

now in this particular scenario what

will happen is that we should try to

reduce this false positive value we

should try to reduce it false positive

value because I understand in this case

in a spam mail detection if it is not a

spam and if it is specified or predicted

as a spam the customer is going to miss

that particular mail which may be a very

important mail itself right so because

of that we should always try to reduce

this false positive value in the case of

this kind of use case that is spam

detection but what about recall suppose

I say that whether the person is having

cancer or not cancer or not okay so my

one value basically specifies that he is

having cancer 0 specifies that he is not

having cancer now in this particular

case we should try to reduce false

negative

why understand this guys this one now

consider that if the person is having a

cancer actually is having a cancer but

he is predicted as that the person is

not having by the model okay so that is

what say so yeah these are natural

values suppose is he he is suffering

from cancer so that what what exactly

the actual value says right but the

model has predicted that the person is

not having a cancer so this may be a

disaster because I can understand if I

get an error in false positive then the

person will go with another for the test

to understand whether he is having

cancer or not but if my model predicted

that even though he had a cancer but

here so he was predicted as he was not

having a cancer so this is the disaster

at that time we should specifically use

recall now in short guys whenever your

false positive is much more important

whenever your false positive is much

more important go and blind

use precision whenever with respect to

your problem statement if your recall

with your false negative is important at

that time you go and use recall I gave

you an example guys okay cancer whether

a person is having cancer or not some

more examples whether tomorrow the stock

market is going to crash or not some

example of precision spam detection

right consider this particular example

try to think always our aim should be to

reduce false positive and false negative

but whether false in the positive is

playing a greater impact or role in that

specific model if we displayed go and

use precision focus on precision if

false negative is actually playing a

greater role or if greater impact it is

having this go and use recall now I want

to introduce you to something bad as f

beta now sometimes in some of the

problem statements guys false positive

and false negative both are very very

important okay in an imbalance data set

I'm saying okay both will be important

at that time we have to consider both

recall and precision and if you want to

reconsider both recall and precision we

basically use FB does score okay FB does

code and sometimes in some of the

problem statements guys even though

recall play a major role that is like

false negative player is major role or a

false positive play a major role you

know some of the problem statement you

should try to combine both precision and

recall to get the most accurate value so

for that particular case we use a as

beta score now here I'm going to define

a F beta school formula for you so that

you'll be able to understand so now let

us go ahead and understand what exactly

is the F beta score guys in F beta

usually the main aim is to select this

beta value okay now how we should go

ahead and select the beta value I'll

just tell you in a minute

but just understand one thing guys this

exactly if I just consider my beta value

is one okay this basically becomes an f1

score okay

this basically becomes an f1 score how

to select when to select beta is equal

to one

I'm just going to tell you in a while

similarly beta values can vary it can

also be less than one

one it can be 0.5 it can be - you know

if it is 0.5 we basically say this as f

0.5 score if it is 2 means basically say

that F 2 score okay now I hope I don't

know whether you have seen this

particular formula guys but just

understand this is my F beta and

initially we need to select this

particular beta value now consider that

it's my beta values 1 now this formula

basically becomes 2 into precision

multiplied by recall divided by

precision plus recall okay and this is

basically called as a if you don't know

about this guy this is called as a

harmonic mean harmonic mean if I replace

this precision recall by X X - x + y so

this will become 2 X Y divided by x + y

I hope you have been formula with this

particular formula itself this was we've

used in some of the linear algebra in

your school days and in your college

days if you remember - XY / x + y I'm

considering precision and X recall as Y

precision over AR x x + y okay now

understand over here

when should we select beta is equal to 1

now understand guys if you have a

problem statement where wherever through

positive sorry not true positive false

positive and false negative both are

equally important both are having a

greater impact at that time you go and

select beta is equal to 1 okay now in

some of the scenarios suppose you're

false positive is having more impact

than the false negative that is then the

type 2 error okay false positive is the

type 1 error at that time you reduce

your beta value to 0.5 between between 0

to 1

usually people select it as 0.5 at that

time this beta value will get converted

to 0.5 so it will be 1 point 1 plus 0.25

this is nothing but 1 point 2 5

multiplied the precision in to recall

divided by this will basically become

0.25 into precision + record so whenever

your false positive is more important

we reduce this beta value now similarly

if my false negative is high false

negative impact is high right that is

for recall if you remember guys okay if

that is high at that time we

specifically increase my beta value

greater than 1 suppose I consider it as

2 right at that time what will happen

again I'll have to try to apply in this

beta value this particular 2 value this

is nothing will but 5 multiplied by

appreciation into recall divided by 4

multiplied by precision plus recall so

if if false negative and false positive

are both important we consider beta is

equal to 1 if false positive is more

important at that time what we do we

reduce the beta value if false negative

is having an higher impact we increase

the beta value and that is how we select

this F beta value and sometimes we say

it as f1 score sometimes we say it as f2

score sometimes we say say it as f 0.5

score okay considering this particular

values and again guys

based on this false negative also

sometimes your beta value ranges between

1 to 10 okay considering where your

false negative is having a greater

impact when you have false visit

positive is having a greater impact you

basically select a value somewhere

between 0 to 1 and that is why you

specifically use F beta whether you want

to combine both precision recall and try

to showcase a particular problem

statement and try to select the right

kind of metrics this each and every

parameter is very very important guys so

I hope you understood this particular

videos guys in the part 2 will be

discussing about Kohan Kappa ROC curve

AUC score PR curve and there are some

more two more metrics which I am going

to discuss in the next part 2 in the

part 3 we will basically be implementing

a practical problem statement to make

you understand all these particular

things so I hope you like this

particular video I know this is too much

just go revise it you get to know each

and everything so yes this was all about

this particular video I hope you liked

it please do subscribe the channel if

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next video have a great day ahead thank

you one at all